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feat(create_mcp): 添加MCP工具输出Schema定义

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添加了固定的输出Schema定义，包含code、message和data字段，
用于规范MCP工具的返回格式，提高API响应的一致性。

- 定义了标准的输出Schema结构
- 包含响应状态码、消息和数据字段
- code和message为必需字段
This commit is contained in:
yuanzhipeng
2026-01-04 10:14:23 +08:00
parent ac403b5e6f
commit 384a1fbcb2
28 changed files with 3408 additions and 51 deletions
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37
lzwcai_mcp_api_converter/lzwcai_mcp_api_converter.log Normal file
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@@ -0,0 +1,37 @@
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.util.logger_config - INFO - [logger_config.py:215] - ================================================================================
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.util.logger_config - INFO - [logger_config.py:216] - 日志系统初始化完成 - 2025-12-30 11:48:23
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.util.logger_config - INFO - [logger_config.py:217] - 日志级别: INFO
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.util.logger_config - INFO - [logger_config.py:218] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_api_converter\lzwcai_mcp_api_converter.log
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.util.logger_config - INFO - [logger_config.py:219] - 控制台输出: False
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.util.logger_config - INFO - [logger_config.py:220] - 文件输出: True
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.util.logger_config - INFO - [logger_config.py:221] - 文件轮转: 最大10MB, 保留5个备份
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.util.logger_config - INFO - [logger_config.py:222] - ================================================================================
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:277] - 开始初始化 MCP 服务器
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:116] - 配置模式: memory
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:124] - 使用内存模式加载配置（多租户支持）
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:135] - 使用环境变量提供的businessUuid: u9ua9ss2l8c
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:139] - 租户配置变量名: businessu9ua9ss2l8c
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:147] - 内存中没有租户 u9ua9ss2l8c 的配置，开始从业务平台获取...
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:168] - 从环境变量bizSysApiIds获取到API IDs: [1970386761072058369, 1970386761185304578, 1970386761583763457, 1970386761420185602]
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:175] - 调用get_business_api_config获取配置，API IDs: [1970386761072058369, 1970386761185304578, 1970386761583763457, 1970386761420185602]
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.business.get_business_api - INFO - [get_business_api.py:193] - 开始获取 4 个API的详情...
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.business.get_business_api - INFO - [get_business_api.py:93] - 成功获取 4 个API详情
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.business.get_business_api - INFO - [get_business_api.py:197] - 开始映射为配置格式...
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.business.get_business_api - INFO - [get_business_api.py:158] - 成功映射 4 个API到配置格式
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.business.get_business_api - INFO - [get_business_api.py:159] - 服务名称: lzwcai_mcp_api_converter
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.business.get_business_api - INFO - [get_business_api.py:160] - 域名URL: https://erp.166bg.com/api
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.business.get_business_api - INFO - [get_business_api.py:161] - 描述: 定时任务列表、定时任务详情、任务列表、任务统计列表（按状态）
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.business.get_business_api - INFO - [get_business_api.py:200] - [SUCCESS] 成功生成API配置！包含 4 个API
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:177] - 成功获取业务API配置，包含 4 个API配置
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:181] - 配置已存储到内存变量: businessu9ua9ss2l8c
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:182] - 当前内存中共有 1 个租户配置
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:292] - 服务器配置 - 名称: lzwcai-mcp-dyntoolapi, 版本: 1.0.0
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.core.api_base - INFO - [api_base.py:262] - 开始处理 4 个API配置
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.core.api_base - INFO - [api_base.py:317] - API配置处理完成，成功处理 4 个配置
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.core.api_base - INFO - [api_base.py:389] - ApiBase初始化完成，共处理 4 个API配置
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:307] - API 基础服务初始化完成，共 4 个API配置
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:506] - 注册API工具插件
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:509] - API工具插件注册完成
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:779] - 配置文件监控功能已禁用（如需启用，请设置环境变量ENABLE_CONFIG_WATCH=true）
2025-12-30 11:48:23 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:578] - 启动STDIO传输模式
2025-12-30 11:48:24 - lzwcai_mcp_api_converter.src.create_mcp - INFO - [create_mcp.py:391] - 返回工具列表，共 4 个工具

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lzwcai_mcp_api_converter/lzwcai_mcp_api_converter/src/create_mcp.py
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@@ -367,12 +367,24 @@ class ApiToolPlugin(ToolPlugin):
tool_name = tool_config["interfaceName"] # 工具名称（拼音格式）
logger.debug(f"注册工具: {tool_name}")
# 定义输出 Schema（先写死一个测试）
output_schema = {
"type": "object",
"properties": {
"code": {"type": "integer", "description": "响应状态码，0表示成功"},
"message": {"type": "string", "description": "响应消息"},
"data": {"type": "object", "description": "响应数据"}
},
"required": ["code", "message"]
}
# 创建MCP工具定义
tools.append(
types.Tool(
name=tool_name, # 工具名称
description=tool_config["schema_description"], # 工具描述（包含参数说明）
inputSchema=tool_config["schema"], # 输入参数的JSON Schema
outputSchema=output_schema, # 输出参数的JSON Schema
)
)

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lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor.log
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@@ -49,3 +49,226 @@
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:128] - API配置: 1 条
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:129] - API配置数组: [{'id': '1981245768769118209', 'businessName': '数字员工列表查询', 'businessDescription': '查询系统中的所有数字员工信息，包括员工ID、名称、类型、状态等核心信息，用于展示数字员工管理列表', 'sqlTemplate': 'SELECT id, employee_id, name, employee_type, status, created_at, updated_at, description FROM digital_employees WHERE is_deleted = 0 ORDER BY created_at DESC', 'parameters': {'type': 'object', 'required': ['employeeId'], 'properties': {'employeeId': {'type': 'number', 'description': '员工ID，用于标识员工的唯一数字标识符', 'examples': [1001, 2002]}}}, 'datasourceId': '32'}]
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:165] - 成功生成 1 个 MCP 工具
2025-12-31 12:57:27 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs
2025-12-31 12:57:27 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:347] - 开始运行 MCP SQL Executor 服务器
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:299] - ============================================================
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:300] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:301] - 版本: 0.1.0
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:302] - ============================================================
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:306] - 环境配置 - Database ID: 16
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:307] - 环境配置 - Skill ID:
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:308] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:309] - ============================================================
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:314] - MCP 服务器已启动，等待客户端连接...
2025-12-31 12:57:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 12:57:28 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 12:57:28 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）...
2025-12-31 12:57:28 - mcp_services - INFO - [main.py:278] - 调用第三方API，skill_id:
2025-12-31 12:57:28 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.11.24:8088/datasource/skill/getBySkillId/ "HTTP/1.1 404 "
2025-12-31 12:57:29 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:97] - API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:98] - 错误响应: {"timestamp":"2025-12-31T12:57:30.248+08:00","status":404,"error":"Not Found","path":"/datasource/skill/getBySkillId/"}
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:292] - API调用失败: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 80, in get_skill_by_id
response.raise_for_status()
File "D:\anaconda3\Lib\site-packages\httpx\_models.py", line 829, in raise_for_status
raise HTTPStatusError(message, request=request, response=self)
httpx.HTTPStatusError: Client error '404 ' for url 'http://192.168.11.24:8088/datasource/skill/getBySkillId/'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/404
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 281, in call_third_party_api
raw_result = get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 208, in get_skill_by_id
return default_client.get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 99, in get_skill_by_id
raise Exception(error_msg)
Exception: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - mcp_services - WARNING - [main.py:131] - API获取失败，降级使用本地配置: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - mcp_services - INFO - [main.py:55] - 成功加载 3 个业务查询配置
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 12:57:29 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:54 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs
2025-12-31 12:57:54 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:347] - 开始运行 MCP SQL Executor 服务器
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:299] - ============================================================
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:300] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:301] - 版本: 0.1.0
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:302] - ============================================================
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:306] - 环境配置 - Database ID: 16
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:307] - 环境配置 - Skill ID:
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:308] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:309] - ============================================================
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:314] - MCP 服务器已启动，等待客户端连接...
2025-12-31 12:57:54 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:55] - 成功加载 3 个业务查询配置
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:123] - 本地配置: 3 条
2025-12-31 12:57:54 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:54 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:31 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 15:00:31 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 15:00:31 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:31 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:34 - mcp_services - INFO - [main.py:329] - MCP 服务器已关闭
2025-12-31 15:00:53 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs
2025-12-31 15:00:53 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:347] - 开始运行 MCP SQL Executor 服务器
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:299] - ============================================================
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:300] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:301] - 版本: 0.1.0
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:302] - ============================================================
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:306] - 环境配置 - Database ID: 29
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:307] - 环境配置 - Skill ID:
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:308] - 环境配置 - Backend Base URL: http://lzwcai-demp-corp-manager:8086
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:309] - ============================================================
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:314] - MCP 服务器已启动，等待客户端连接...
2025-12-31 15:00:54 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 15:00:54 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 15:00:54 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）...
2025-12-31 15:00:54 - mcp_services - INFO - [main.py:278] - 调用第三方API，skill_id:
2025-12-31 15:00:54 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/
2025-12-31 15:00:56 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:103] - API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
2025-12-31 15:00:56 - mcp_services - ERROR - [main.py:292] - API调用失败: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
Traceback (most recent call last):
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 101, in map_httpcore_exceptions
yield
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 250, in handle_request
resp = self._pool.handle_request(req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 216, in handle_request
raise exc from None
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 196, in handle_request
response = connection.handle_request(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 99, in handle_request
raise exc
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 76, in handle_request
stream = self._connect(request)
^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 122, in _connect
stream = self._network_backend.connect_tcp(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_backends\sync.py", line 205, in connect_tcp
with map_exceptions(exc_map):
^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__
self.gen.throw(value)
File "D:\anaconda3\Lib\site-packages\httpcore\_exceptions.py", line 14, in map_exceptions
raise to_exc(exc) from exc
httpcore.ConnectError: [Errno 11001] getaddrinfo failed
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 74, in get_skill_by_id
response = self.client.get(
^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1053, in get
return self.request(
^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 825, in request
return self.send(request, auth=auth, follow_redirects=follow_redirects)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 914, in send
response = self._send_handling_auth(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 942, in _send_handling_auth
response = self._send_handling_redirects(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 979, in _send_handling_redirects
response = self._send_single_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1014, in _send_single_request
response = transport.handle_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 249, in handle_request
with map_httpcore_exceptions():
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__
self.gen.throw(value)
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 118, in map_httpcore_exceptions
raise mapped_exc(message) from exc
httpx.ConnectError: [Errno 11001] getaddrinfo failed
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 281, in call_third_party_api
raw_result = get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 208, in get_skill_by_id
return default_client.get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 104, in get_skill_by_id
raise Exception(error_msg)
Exception: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
2025-12-31 15:00:56 - mcp_services - WARNING - [main.py:131] - API获取失败，降级使用本地配置: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:55] - 成功加载 0 个业务查询配置
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:165] - 成功生成 0 个 MCP 工具
2025-12-31 15:00:56 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:165] - 成功生成 0 个 MCP 工具

274
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor_daily.log
View File

@@ -1,51 +1,223 @@
2025-10-23 15:05:51 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs
2025-10-23 15:05:51 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:341] - 开始运行 MCP SQL Executor 服务器
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:293] - ============================================================
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:294] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:295] - 版本: 0.1.0
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:296] - ============================================================
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:300] - 环境配置 - Database ID: 29
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:301] - 环境配置 - Skill ID: 1981195682443014146
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:302] - 环境配置 - Backend Base URL: http://192.168.2.236:8088
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:303] - ============================================================
2025-10-23 15:05:51 - mcp_services - INFO - [main.py:308] - MCP 服务器已启动，等待客户端连接...
2025-10-23 15:05:52 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-10-23 15:05:52 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-10-23 15:05:52 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）...
2025-10-23 15:05:52 - mcp_services - INFO - [main.py:272] - 调用第三方API，skill_id: 1981195682443014146
2025-10-23 15:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://192.168.2.236:8088/datasource/skill/getBySkillId/1981195682443014146
2025-10-23 15:05:52 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/datasource/skill/getBySkillId/1981195682443014146 "HTTP/1.1 200 "
2025-10-23 15:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:85] - API调用成功: http://192.168.2.236:8088/datasource/skill/getBySkillId/1981195682443014146
2025-10-23 15:05:52 - mcp_services - INFO - [main.py:277] - 成功{'msg': '查询失败: 技能不存在: 1981195682443014146', 'code': 500}
2025-10-23 15:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:297] - 成功处理 0 条技能数据
2025-10-23 15:05:52 - mcp_services - INFO - [main.py:282] - 成功获取并处理 0 条数据
2025-10-23 15:05:52 - mcp_services - INFO - [main.py:128] - API配置: 0 条
2025-10-23 15:05:52 - mcp_services - INFO - [main.py:129] - API配置数组: []
2025-10-23 15:05:52 - mcp_services - INFO - [main.py:165] - 成功生成 0 个 MCP 工具
2025-10-23 15:08:30 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs
2025-10-23 15:08:30 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:341] - 开始运行 MCP SQL Executor 服务器
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:293] - ============================================================
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:294] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:295] - 版本: 0.1.0
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:296] - ============================================================
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:300] - 环境配置 - Database ID: 29
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:301] - 环境配置 - Skill ID: 1981245768471322626
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:302] - 环境配置 - Backend Base URL: http://192.168.2.236:8088
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:303] - ============================================================
2025-10-23 15:08:30 - mcp_services - INFO - [main.py:308] - MCP 服务器已启动，等待客户端连接...
2025-10-23 15:08:31 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）...
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:272] - 调用第三方API，skill_id: 1981245768471322626
2025-10-23 15:08:31 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://192.168.2.236:8088/datasource/skill/getBySkillId/1981245768471322626
2025-10-23 15:08:31 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/datasource/skill/getBySkillId/1981245768471322626 "HTTP/1.1 200 "
2025-10-23 15:08:31 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:85] - API调用成功: http://192.168.2.236:8088/datasource/skill/getBySkillId/1981245768471322626
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:277] - 成功{'msg': '查询成功', 'code': 200, 'data': [{'id': '1981245768769118209', 'createBy': 'yy8z6', 'createTime': '2025-10-23 14:26:16', 'updateBy': None, 'updateTime': None, 'serviceId': '1981245768475516930', 'uniqueName': '数字员工技能描述-SQL服务_数字员工列表查询', 'name': '数字员工列表查询', 'description': '查询系统中的所有数字员工信息，包括员工ID、名称、类型、状态等核心信息，用于展示数字员工管理列表', 'visualizable': 1, 'toolPrompt': '查询数字员工列表', 'toolType': 'sql', 'datasourceId': '32', 'sqlTemplate': 'SELECT id, employee_id, name, employee_type, status, created_at, updated_at, description FROM digital_employees WHERE is_deleted = 0 ORDER BY created_at DESC', 'sqlParams': '{"type":"object","required":[],"properties":{}}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '889,890,891,892,893,894,895,896', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{}', 'outputJsonSchema': '{"type":"object","properties":{"data":{"type":"array"}}}', 'lastExecutionTime': None}]}
2025-10-23 15:08:31 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:283] - 技能 数字员工列表查询 (ID: 1981245768769118209) 的sqlParams为空，使用默认员工ID参数
2025-10-23 15:08:31 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:297] - 成功处理 1 条技能数据
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:282] - 成功获取并处理 1 条数据
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:128] - API配置: 1 条
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:129] - API配置数组: [{'id': '1981245768769118209', 'businessName': '数字员工列表查询', 'businessDescription': '查询系统中的所有数字员工信息，包括员工ID、名称、类型、状态等核心信息，用于展示数字员工管理列表', 'sqlTemplate': 'SELECT id, employee_id, name, employee_type, status, created_at, updated_at, description FROM digital_employees WHERE is_deleted = 0 ORDER BY created_at DESC', 'parameters': {'type': 'object', 'required': ['employeeId'], 'properties': {'employeeId': {'type': 'number', 'description': '员工ID，用于标识员工的唯一数字标识符', 'examples': [1001, 2002]}}}, 'datasourceId': '32'}]
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:165] - 成功生成 1 个 MCP 工具
2025-12-31 12:57:27 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs
2025-12-31 12:57:27 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:347] - 开始运行 MCP SQL Executor 服务器
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:299] - ============================================================
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:300] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:301] - 版本: 0.1.0
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:302] - ============================================================
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:306] - 环境配置 - Database ID: 16
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:307] - 环境配置 - Skill ID:
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:308] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:309] - ============================================================
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:314] - MCP 服务器已启动，等待客户端连接...
2025-12-31 12:57:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 12:57:28 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 12:57:28 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）...
2025-12-31 12:57:28 - mcp_services - INFO - [main.py:278] - 调用第三方API，skill_id:
2025-12-31 12:57:28 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.11.24:8088/datasource/skill/getBySkillId/ "HTTP/1.1 404 "
2025-12-31 12:57:29 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:97] - API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:98] - 错误响应: {"timestamp":"2025-12-31T12:57:30.248+08:00","status":404,"error":"Not Found","path":"/datasource/skill/getBySkillId/"}
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:292] - API调用失败: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 80, in get_skill_by_id
response.raise_for_status()
File "D:\anaconda3\Lib\site-packages\httpx\_models.py", line 829, in raise_for_status
raise HTTPStatusError(message, request=request, response=self)
httpx.HTTPStatusError: Client error '404 ' for url 'http://192.168.11.24:8088/datasource/skill/getBySkillId/'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/404
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 281, in call_third_party_api
raw_result = get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 208, in get_skill_by_id
return default_client.get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 99, in get_skill_by_id
raise Exception(error_msg)
Exception: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - mcp_services - WARNING - [main.py:131] - API获取失败，降级使用本地配置: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - mcp_services - INFO - [main.py:55] - 成功加载 3 个业务查询配置
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 12:57:29 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:54 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs
2025-12-31 12:57:54 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:347] - 开始运行 MCP SQL Executor 服务器
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:299] - ============================================================
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:300] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:301] - 版本: 0.1.0
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:302] - ============================================================
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:306] - 环境配置 - Database ID: 16
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:307] - 环境配置 - Skill ID:
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:308] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:309] - ============================================================
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:314] - MCP 服务器已启动，等待客户端连接...
2025-12-31 12:57:54 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:55] - 成功加载 3 个业务查询配置
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:123] - 本地配置: 3 条
2025-12-31 12:57:54 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:54 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:31 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 15:00:31 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 15:00:31 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:31 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:34 - mcp_services - INFO - [main.py:329] - MCP 服务器已关闭
2025-12-31 15:00:53 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs
2025-12-31 15:00:53 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:347] - 开始运行 MCP SQL Executor 服务器
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:299] - ============================================================
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:300] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:301] - 版本: 0.1.0
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:302] - ============================================================
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:306] - 环境配置 - Database ID: 29
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:307] - 环境配置 - Skill ID:
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:308] - 环境配置 - Backend Base URL: http://lzwcai-demp-corp-manager:8086
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:309] - ============================================================
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:314] - MCP 服务器已启动，等待客户端连接...
2025-12-31 15:00:54 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 15:00:54 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 15:00:54 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）...
2025-12-31 15:00:54 - mcp_services - INFO - [main.py:278] - 调用第三方API，skill_id:
2025-12-31 15:00:54 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/
2025-12-31 15:00:56 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:103] - API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
2025-12-31 15:00:56 - mcp_services - ERROR - [main.py:292] - API调用失败: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
Traceback (most recent call last):
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 101, in map_httpcore_exceptions
yield
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 250, in handle_request
resp = self._pool.handle_request(req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 216, in handle_request
raise exc from None
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 196, in handle_request
response = connection.handle_request(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 99, in handle_request
raise exc
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 76, in handle_request
stream = self._connect(request)
^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 122, in _connect
stream = self._network_backend.connect_tcp(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_backends\sync.py", line 205, in connect_tcp
with map_exceptions(exc_map):
^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__
self.gen.throw(value)
File "D:\anaconda3\Lib\site-packages\httpcore\_exceptions.py", line 14, in map_exceptions
raise to_exc(exc) from exc
httpcore.ConnectError: [Errno 11001] getaddrinfo failed
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 74, in get_skill_by_id
response = self.client.get(
^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1053, in get
return self.request(
^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 825, in request
return self.send(request, auth=auth, follow_redirects=follow_redirects)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 914, in send
response = self._send_handling_auth(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 942, in _send_handling_auth
response = self._send_handling_redirects(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 979, in _send_handling_redirects
response = self._send_single_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1014, in _send_single_request
response = transport.handle_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 249, in handle_request
with map_httpcore_exceptions():
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__
self.gen.throw(value)
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 118, in map_httpcore_exceptions
raise mapped_exc(message) from exc
httpx.ConnectError: [Errno 11001] getaddrinfo failed
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 281, in call_third_party_api
raw_result = get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 208, in get_skill_by_id
return default_client.get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 104, in get_skill_by_id
raise Exception(error_msg)
Exception: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
2025-12-31 15:00:56 - mcp_services - WARNING - [main.py:131] - API获取失败，降级使用本地配置: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:55] - 成功加载 0 个业务查询配置
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:165] - 成功生成 0 个 MCP 工具
2025-12-31 15:00:56 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:165] - 成功生成 0 个 MCP 工具

158
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor_error.log
View File

@@ -0,0 +1,158 @@
2025-12-31 12:57:29 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:97] - API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:98] - 错误响应: {"timestamp":"2025-12-31T12:57:30.248+08:00","status":404,"error":"Not Found","path":"/datasource/skill/getBySkillId/"}
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:292] - API调用失败: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 80, in get_skill_by_id
response.raise_for_status()
File "D:\anaconda3\Lib\site-packages\httpx\_models.py", line 829, in raise_for_status
raise HTTPStatusError(message, request=request, response=self)
httpx.HTTPStatusError: Client error '404 ' for url 'http://192.168.11.24:8088/datasource/skill/getBySkillId/'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/404
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 281, in call_third_party_api
raw_result = get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 208, in get_skill_by_id
return default_client.get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 99, in get_skill_by_id
raise Exception(error_msg)
Exception: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:54 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:54 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:31 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:31 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:56 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:103] - API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
2025-12-31 15:00:56 - mcp_services - ERROR - [main.py:292] - API调用失败: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
Traceback (most recent call last):
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 101, in map_httpcore_exceptions
yield
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 250, in handle_request
resp = self._pool.handle_request(req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 216, in handle_request
raise exc from None
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 196, in handle_request
response = connection.handle_request(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 99, in handle_request
raise exc
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 76, in handle_request
stream = self._connect(request)
^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 122, in _connect
stream = self._network_backend.connect_tcp(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_backends\sync.py", line 205, in connect_tcp
with map_exceptions(exc_map):
^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__
self.gen.throw(value)
File "D:\anaconda3\Lib\site-packages\httpcore\_exceptions.py", line 14, in map_exceptions
raise to_exc(exc) from exc
httpcore.ConnectError: [Errno 11001] getaddrinfo failed
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 74, in get_skill_by_id
response = self.client.get(
^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1053, in get
return self.request(
^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 825, in request
return self.send(request, auth=auth, follow_redirects=follow_redirects)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 914, in send
response = self._send_handling_auth(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 942, in _send_handling_auth
response = self._send_handling_redirects(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 979, in _send_handling_redirects
response = self._send_single_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1014, in _send_single_request
response = transport.handle_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 249, in handle_request
with map_httpcore_exceptions():
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__
self.gen.throw(value)
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 118, in map_httpcore_exceptions
raise mapped_exc(message) from exc
httpx.ConnectError: [Errno 11001] getaddrinfo failed
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 281, in call_third_party_api
raw_result = get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 208, in get_skill_by_id
return default_client.get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 104, in get_skill_by_id
raise Exception(error_msg)
Exception: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed

205
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/mcp_services.log
View File

@@ -34,3 +34,208 @@
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:128] - API配置: 1 条
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:129] - API配置数组: [{'id': '1981245768769118209', 'businessName': '数字员工列表查询', 'businessDescription': '查询系统中的所有数字员工信息，包括员工ID、名称、类型、状态等核心信息，用于展示数字员工管理列表', 'sqlTemplate': 'SELECT id, employee_id, name, employee_type, status, created_at, updated_at, description FROM digital_employees WHERE is_deleted = 0 ORDER BY created_at DESC', 'parameters': {'type': 'object', 'required': ['employeeId'], 'properties': {'employeeId': {'type': 'number', 'description': '员工ID，用于标识员工的唯一数字标识符', 'examples': [1001, 2002]}}}, 'datasourceId': '32'}]
2025-10-23 15:08:31 - mcp_services - INFO - [main.py:165] - 成功生成 1 个 MCP 工具
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:347] - 开始运行 MCP SQL Executor 服务器
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:299] - ============================================================
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:300] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:301] - 版本: 0.1.0
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:302] - ============================================================
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:306] - 环境配置 - Database ID: 16
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:307] - 环境配置 - Skill ID:
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:308] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:309] - ============================================================
2025-12-31 12:57:27 - mcp_services - INFO - [main.py:314] - MCP 服务器已启动，等待客户端连接...
2025-12-31 12:57:28 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 12:57:28 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）...
2025-12-31 12:57:28 - mcp_services - INFO - [main.py:278] - 调用第三方API，skill_id:
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:292] - API调用失败: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 80, in get_skill_by_id
response.raise_for_status()
File "D:\anaconda3\Lib\site-packages\httpx\_models.py", line 829, in raise_for_status
raise HTTPStatusError(message, request=request, response=self)
httpx.HTTPStatusError: Client error '404 ' for url 'http://192.168.11.24:8088/datasource/skill/getBySkillId/'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/404
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 281, in call_third_party_api
raw_result = get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 208, in get_skill_by_id
return default_client.get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 99, in get_skill_by_id
raise Exception(error_msg)
Exception: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - mcp_services - WARNING - [main.py:131] - API获取失败，降级使用本地配置: API请求失败 (HTTP 404): http://192.168.11.24:8088/datasource/skill/getBySkillId/
2025-12-31 12:57:29 - mcp_services - INFO - [main.py:55] - 成功加载 3 个业务查询配置
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:29 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:347] - 开始运行 MCP SQL Executor 服务器
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:299] - ============================================================
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:300] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:301] - 版本: 0.1.0
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:302] - ============================================================
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:306] - 环境配置 - Database ID: 16
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:307] - 环境配置 - Skill ID:
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:308] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:309] - ============================================================
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:314] - MCP 服务器已启动，等待客户端连接...
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:55] - 成功加载 3 个业务查询配置
2025-12-31 12:57:54 - mcp_services - INFO - [main.py:123] - 本地配置: 3 条
2025-12-31 12:57:54 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 12:57:54 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:31 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 15:00:31 - mcp_services - ERROR - [main.py:92] - 生成工具模式失败: 2006300000000000001, 错误: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:31 - mcp_services - ERROR - [main.py:170] - 列出工具失败: 'businessName'
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 162, in handle_list_tools
tool = generate_tool_schema_from_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 80, in generate_tool_schema_from_query
tool_name = query['businessName']
~~~~~^^^^^^^^^^^^^^^^
KeyError: 'businessName'
2025-12-31 15:00:34 - mcp_services - INFO - [main.py:329] - MCP 服务器已关闭
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:347] - 开始运行 MCP SQL Executor 服务器
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:299] - ============================================================
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:300] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:301] - 版本: 0.1.0
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:302] - ============================================================
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:306] - 环境配置 - Database ID: 29
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:307] - 环境配置 - Skill ID:
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:308] - 环境配置 - Backend Base URL: http://lzwcai-demp-corp-manager:8086
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:309] - ============================================================
2025-12-31 15:00:53 - mcp_services - INFO - [main.py:314] - MCP 服务器已启动，等待客户端连接...
2025-12-31 15:00:54 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 15:00:54 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）...
2025-12-31 15:00:54 - mcp_services - INFO - [main.py:278] - 调用第三方API，skill_id:
2025-12-31 15:00:56 - mcp_services - ERROR - [main.py:292] - API调用失败: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
Traceback (most recent call last):
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 101, in map_httpcore_exceptions
yield
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 250, in handle_request
resp = self._pool.handle_request(req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 216, in handle_request
raise exc from None
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 196, in handle_request
response = connection.handle_request(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 99, in handle_request
raise exc
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 76, in handle_request
stream = self._connect(request)
^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 122, in _connect
stream = self._network_backend.connect_tcp(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpcore\_backends\sync.py", line 205, in connect_tcp
with map_exceptions(exc_map):
^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__
self.gen.throw(value)
File "D:\anaconda3\Lib\site-packages\httpcore\_exceptions.py", line 14, in map_exceptions
raise to_exc(exc) from exc
httpcore.ConnectError: [Errno 11001] getaddrinfo failed
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 74, in get_skill_by_id
response = self.client.get(
^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1053, in get
return self.request(
^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 825, in request
return self.send(request, auth=auth, follow_redirects=follow_redirects)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 914, in send
response = self._send_handling_auth(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 942, in _send_handling_auth
response = self._send_handling_redirects(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 979, in _send_handling_redirects
response = self._send_single_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1014, in _send_single_request
response = transport.handle_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 249, in handle_request
with map_httpcore_exceptions():
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__
self.gen.throw(value)
File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 118, in map_httpcore_exceptions
raise mapped_exc(message) from exc
httpx.ConnectError: [Errno 11001] getaddrinfo failed
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 281, in call_third_party_api
raw_result = get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 208, in get_skill_by_id
return default_client.get_skill_by_id(skill_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 104, in get_skill_by_id
raise Exception(error_msg)
Exception: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
2025-12-31 15:00:56 - mcp_services - WARNING - [main.py:131] - API获取失败，降级使用本地配置: API请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:55] - 成功加载 0 个业务查询配置
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:165] - 成功生成 0 个 MCP 工具
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
2025-12-31 15:00:56 - mcp_services - INFO - [main.py:165] - 成功生成 0 个 MCP 工具

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# lzwcai-mcp-sqlexecutor
一个基于 MCP (Model Context Protocol) 的 SQL 查询执行服务器，支持从 JSON 配置文件动态生成查询工具。
## 功能特性
- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具
- 🔧 灵活配置：支持自定义业务查询和参数验证
- 📝 完整日志：详细的操作日志记录
- 🌐 中文支持：工具名称自动转换为拼音
## 安装
### 使用 pip 安装
```bash
pip install lzwcai-mcp-sqlexecutor
```
### 从源码安装
```bash
git clone <repository-url>
cd lzwcai_mcp_sqlexecutor
pip install -e .
```
### 使用 uv 安装（推荐）
```bash
uv pip install lzwcai-mcp-sqlexecutor
```
## 使用方法
### 命令行启动
安装后，可以直接通过命令启动：
```bash
lzwcai-mcp-sqlexecutor
```
### 作为 Python 模块运行
```bash
python -m lzwcai_mcp_sqlexecutor.main
```
### 配置到 MCP 客户端
在你的 MCP 客户端配置文件中添加：
```json
{
"mcpServers": {
"lzwcai-sqlexecutor": {
"command": "lzwcai-mcp-sqlexecutor"
}
}
}
```
## 配置说明
### businessQueries.json
在 `businessQueries.json` 中定义你的业务查询：
```json
[
{
"id": "query-001",
"businessName": "用户订单查询",
"businessDescription": "根据用户ID查询订单信息",
"sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}",
"parameters": {
"type": "object",
"required": ["userId"],
"properties": {
"userId": {
"type": "integer",
"description": "用户的唯一标识符",
"examples": [10086]
}
}
}
}
]
```
## 开发
### 依赖项
- Python >= 3.13
- httpx >= 0.28.1
- mcp[cli] >= 1.10.1
- pypinyin >= 0.53.0
### 本地开发
```bash
# 克隆仓库
git clone <repository-url>
cd lzwcai_mcp_sqlexecutor
# 安装开发依赖
pip install -e .
# 运行服务器
python -m lzwcai_mcp_sqlexecutor.main
```
## 构建与发布
### 使用 build 构建
```bash
pip install build
python -m build
```
### 发布到 PyPI
```bash
pip install twine
twine upload dist/*
```
## 许可证
MIT License
## 作者
lzwcai

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# Python-generated files
__pycache__/
*.py[oc]
build/
dist/
wheels/
*.egg-info
# Virtual environments
.venv

1
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3.13

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# lzwcai-mcp-sqlexecutor
一个基于 MCP (Model Context Protocol) 的 SQL 查询执行服务器，支持从 JSON 配置文件动态生成查询工具。
## 功能特性
- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具
- 🔧 灵活配置：支持自定义业务查询和参数验证
- 📝 完整日志：详细的操作日志记录（仅输出到文件，不干扰MCP通信）
- 🌐 中文支持：工具名称自动转换为拼音
## 安装
### 使用 pip 安装
```bash
pip install lzwcai-mcp-sqlexecutor
```
### 从源码安装
```bash
git clone <repository-url>
cd lzwcai_mcp_sqlexecutor
pip install -e .
```
### 使用 uv 安装（推荐）
```bash
uv pip install lzwcai-mcp-sqlexecutor
```
## 使用方法
### 命令行启动
安装后，可以直接通过命令启动：
```bash
lzwcai-mcp-sqlexecutor
```
### 作为 Python 模块运行
```bash
python -m lzwcai_mcp_sqlexecutor.main
```
### 配置到 MCP 客户端
在你的 MCP 客户端配置文件中添加：
```json
{
"mcpServers": {
"lzwcai-sqlexecutor": {
"command": "lzwcai-mcp-sqlexecutor"
}
}
}
```
## 配置说明
### businessQueries.json
在 `businessQueries.json` 中定义你的业务查询：
```json
[
{
"id": "query-001",
"businessName": "用户订单查询",
"businessDescription": "根据用户ID查询订单信息",
"sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}",
"parameters": {
"type": "object",
"required": ["userId"],
"properties": {
"userId": {
"type": "integer",
"description": "用户的唯一标识符",
"examples": [10086]
}
}
}
}
]
```
## 开发
### 依赖项
- Python >= 3.13
- httpx >= 0.28.1
- mcp[cli] >= 1.10.1
- pypinyin >= 0.53.0
### 本地开发
```bash
# 克隆仓库
git clone <repository-url>
cd lzwcai_mcp_sqlexecutor
# 安装开发依赖
pip install -e .
# 运行服务器
python -m lzwcai_mcp_sqlexecutor.main
```
## 构建与发布
### 使用 build 构建
```bash
pip install build
python -m build
```
### 发布到 PyPI
```bash
pip install twine
twine upload dist/*
```
## 常见问题
### MCP Inspector 显示 JSON 解析错误
如果在使用 MCP Inspector 测试时遇到 `SyntaxError: Unexpected non-whitespace character after JSON` 错误，这是因为：
1. **原因**：MCP 协议使用 stdio（标准输入输出）进行 JSON-RPC 通信，任何输出到 stdout 的内容（如 print 语句或控制台日志）都会破坏 JSON 格式。
2. **解决方案**：本服务器已将所有日志输出配置为仅写入文件（位于 `logs/` 目录），不输出到控制台。日志文件包括：
- `lzwcai_mcp_sqlexecutor.log` - 主日志文件
- `lzwcai_mcp_sqlexecutor_error.log` - 错误日志
- `lzwcai_mcp_sqlexecutor_daily.log` - 按日期滚动的日志
- `mcp_services.log` - MCP 服务专用日志
3. **查看日志**：如果需要调试，请查看 `logs/` 目录下的日志文件。
## 许可证
MIT License
## 作者
lzwcai

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"""
lzwcai-mcp-sqlexecutor - MCP server for executing business SQL queries
"""
__version__ = "0.1.2"
__author__ = "lzwcai"
__all__ = []

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[
{
"id": "2006300000000000000",
"businessName": "查询列表",
"businessDescription": "查询所有工单列表，返回fact_work_order表的全部数据",
"datasourceId": "19",
"sqlTemplate": "SELECT * FROM fact_work_order",
"parameters": {}
},
{
"id": "2006300000000000004",
"businessName": "工单详情",
"businessDescription": "根据工单编号查询工单详情，包括工单基本信息、产品信息、委外供应商、工艺路线等",
"datasourceId": "19",
"sqlTemplate": "SELECT wo.work_order_id, wo.work_order_number AS 工单编号, wo.status AS 工单状态, wo.planned_qty AS 计划数量, wo.completed_qty AS 完成数量, wo.operation_good_qty AS 工序良品数, wo.operation_bad_qty AS 工序不良品数, wo.qc_good_qty AS 质检良品数, wo.qc_bad_qty AS 质检不良品数, wo.source_doc_number AS 来源单据编号, wo.event_time_utc AS 事件时间, p.product_code AS 产品编码, p.product_name AS 产品名称, p.product_spec AS 产品规格, s.supplier_name AS 委外供应商, r.routing_name AS 工艺路线 FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = true LEFT JOIN dim_supplier s ON wo.supplier_id = s.supplier_id AND s.is_current = true LEFT JOIN dim_routing r ON wo.routing_id = r.routing_id AND r.is_current = true WHERE wo.work_order_number = '{workOrderNumber}'",
"parameters": {
"workOrderNumber": {
"type": "string",
"description": "工单编号",
"required": true,
"examples": [
"WO1311343859"
]
}
}
},
{
"id": "2006300000000000005",
"businessName": "工单执行进度与异常节点看板",
"businessDescription": "工单执行进度与异常节点看板，展示工单完工进度、工序良率、质检良率、异常标识等关键监控指标",
"datasourceId": "19",
"sqlTemplate": "WITH work_order_progress AS (SELECT wo.work_order_number, wo.status AS work_order_status, wo.planned_qty, wo.completed_qty, wo.operation_good_qty, wo.operation_bad_qty, wo.qc_good_qty, wo.qc_bad_qty, wo.source_doc_number AS sales_order_number, wo.event_time_utc, p.product_code, p.product_name, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 2) ELSE 0 END AS completion_pct, CASE WHEN (wo.operation_good_qty + wo.operation_bad_qty) > 0 THEN ROUND(wo.operation_good_qty * 100.0 / (wo.operation_good_qty + wo.operation_bad_qty), 2) ELSE NULL END AS operation_yield_pct, CASE WHEN (wo.qc_good_qty + wo.qc_bad_qty) > 0 THEN ROUND(wo.qc_good_qty * 100.0 / (wo.qc_good_qty + wo.qc_bad_qty), 2) ELSE NULL END AS qc_yield_pct FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = true), task_summary AS (SELECT work_order_number, COUNT(*) AS total_tasks, SUM(CASE WHEN actual_end_time_utc IS NOT NULL THEN 1 ELSE 0 END) AS completed_tasks, SUM(CASE WHEN actual_start_time_utc IS NOT NULL AND actual_end_time_utc IS NULL THEN 1 ELSE 0 END) AS in_progress_tasks, SUM(CASE WHEN actual_start_time_utc IS NULL THEN 1 ELSE 0 END) AS pending_tasks, SUM(bad_qty) AS total_bad_qty FROM fact_operation_task GROUP BY work_order_number), labor_summary AS (SELECT work_order_number, SUM(report_qty) AS total_report_qty, SUM(good_qty) AS total_good_qty, SUM(bad_qty) AS total_bad_qty, SUM(duration_minutes) AS total_work_minutes FROM fact_labor_report GROUP BY work_order_number), qc_summary AS (SELECT work_order_number, COUNT(*) AS qc_record_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection WHERE work_order_number IS NOT NULL GROUP BY work_order_number) SELECT wp.work_order_number AS 工单编号, wp.product_code AS 产品编码, wp.product_name AS 产品名称, wp.sales_order_number AS 关联销售订单, wp.work_order_status AS 工单状态, wp.planned_qty AS 计划数量, wp.completed_qty AS 完成数量, wp.completion_pct AS 完工进度百分比, COALESCE(ts.total_tasks, 0) AS 工序总数, COALESCE(ts.completed_tasks, 0) AS 已完成工序, COALESCE(ts.in_progress_tasks, 0) AS 进行中工序, COALESCE(ts.pending_tasks, 0) AS 待开始工序, COALESCE(ls.total_report_qty, 0) AS 累计报工数, COALESCE(ls.total_work_minutes, 0) AS 累计工时_分钟, wp.operation_yield_pct AS 工序良率, wp.qc_yield_pct AS 质检良率, COALESCE(qc.total_pass_qty, 0) AS 质检通过数, COALESCE(qc.total_fail_qty, 0) AS 质检不通过数, CASE WHEN wp.completion_pct < 50 AND wp.work_order_status = 'STARTED' THEN '进度滞后' WHEN wp.operation_yield_pct < 90 THEN '工序良率异常' WHEN wp.qc_yield_pct < 95 THEN '质检良率异常' WHEN ts.total_bad_qty > 0 THEN '存在不良品' ELSE '正常' END AS 异常标识, wp.event_time_utc AS 最后更新时间 FROM work_order_progress wp LEFT JOIN task_summary ts ON wp.work_order_number = ts.work_order_number LEFT JOIN labor_summary ls ON wp.work_order_number = ls.work_order_number LEFT JOIN qc_summary qc ON wp.work_order_number = qc.work_order_number ORDER BY CASE wp.work_order_status WHEN 'STARTED' THEN 1 WHEN 'RELEASED' THEN 2 WHEN 'PLANNED' THEN 3 ELSE 4 END, wp.event_time_utc DESC",
"parameters": {}
}
]

358
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from pathlib import Path
from typing import Any
import asyncio
import logging
# 支持直接运行和模块导入两种方式
try:
from .utils import load_json, generate_tool_name, generate_input_schema
from .utils import get_skill_by_id, DataSourceAPIClient, process_skill_response, test_sql_with_schema
from .utils import get_database_id, get_skill_id, get_env_config
from .utils.logger_config import logger_config
except ImportError:
from utils import load_json, generate_tool_name, generate_input_schema
from utils import get_skill_by_id, DataSourceAPIClient, process_skill_response, test_sql_with_schema
from utils import get_database_id, get_skill_id, get_env_config
from utils.logger_config import logger_config
from mcp.server.models import InitializationOptions
from mcp.server import NotificationOptions, Server
import mcp.types as types
# 初始化 MCP 专用日志器
mcp_logger = logger_config.setup_mcp_logging()
# ========== 数据源配置 ==========
# 数据源类型常量
DATA_SOURCE_API = "api" # 仅使用API数据
DATA_SOURCE_LOCAL = "local" # 仅使用本地JSON数据
DATA_SOURCE_BOTH = "both" # 合并本地和API数据
# 默认数据源（可修改）
DEFAULT_DATA_SOURCE = DATA_SOURCE_LOCAL
# ================================
def get_queries():
"""
获取业务查询配置
Returns:
list: 包含所有业务查询配置的列表
"""
try:
# 获取当前文件所在目录
current_dir = Path(__file__).parent
# 构建 businessQueries.json 的路径
json_path = current_dir / "businessQueries.json"
mcp_logger.debug(f"正在读取业务查询配置文件: {json_path}")
# 使用 load_json 方法读取 JSON 文件
queries = load_json(json_path)
mcp_logger.info(f"成功加载 {len(queries)} 个业务查询配置")
return queries
except Exception as e:
mcp_logger.error(f"加载业务查询配置失败: {e}", exc_info=True)
raise
def generate_tool_schema_from_query(query: dict) -> types.Tool:
"""
根据查询配置生成 MCP 工具模式
Args:
query: 单个查询配置字典
Returns:
types.Tool: MCP 工具对象
"""
try:
# 获取参数定义并生成 inputSchema
parameters = query.get('parameters', {})
input_schema = generate_input_schema(parameters)
# 生成工具名称（格式: tool_拼音_id）
# tool_name = generate_tool_name(query['businessName'], query['id'])
tool_name = query['businessName']
# 构建工具描述，包含业务名称和业务描述
description = f"{query['businessName']}: {query['businessDescription']}"
mcp_logger.debug(f"生成工具模式: {tool_name} - {query['businessName']}")
return types.Tool(
name=tool_name,
description=description,
inputSchema=input_schema
)
except Exception as e:
mcp_logger.error(f"生成工具模式失败: {query.get('id', 'unknown')}, 错误: {e}", exc_info=True)
raise
# 创建 MCP 服务器实例
server = Server("lzwcai-mcp-sqlexecutor")
# 缓存查询配置，避免重复加载
_queries_cache = None
async def get_queries_cache(source: str = None):
"""
获取或初始化查询配置缓存
Args:
source: 数据源类型（默认使用 DEFAULT_DATA_SOURCE）
- "api": 仅使用API数据
- "local": 仅使用本地JSON数据
- "both": 合并本地和API数据
Returns:
查询配置列表
"""
global _queries_cache
if _queries_cache is None:
source = source or DEFAULT_DATA_SOURCE
mcp_logger.info(f"初始化查询配置（数据源: {source}）...")
if source == DATA_SOURCE_LOCAL:
_queries_cache = get_queries()
mcp_logger.info(f"本地配置: {len(_queries_cache)} 条")
elif source == DATA_SOURCE_API:
try:
_queries_cache = await call_third_party_api()
mcp_logger.info(f"API配置: {len(_queries_cache)} 条")
mcp_logger.info(f"API配置数组: {_queries_cache}")
except Exception as e:
mcp_logger.warning(f"API获取失败，降级使用本地配置: {e}")
_queries_cache = get_queries()
else: # DATA_SOURCE_BOTH
local = get_queries()
try:
api = await call_third_party_api()
except Exception as e:
mcp_logger.warning(f"API获取失败: {e}")
api = []
_queries_cache = local + api
mcp_logger.info(f"配置总数: {len(_queries_cache)} 条（本地{len(local)}+API{len(api)}）")
return _queries_cache
@server.list_tools()
async def handle_list_tools() -> list[types.Tool]:
"""
列出所有动态生成的 MCP 工具
Returns:
list[types.Tool]: 所有可用的工具列表
"""
try:
mcp_logger.info("收到列出工具请求")
queries = await get_queries_cache()
tools = []
for query in queries:
tool = generate_tool_schema_from_query(query)
tools.append(tool)
mcp_logger.info(f"成功生成 {len(tools)} 个 MCP 工具")
mcp_logger.debug(f"工具列表: {[tool.name for tool in tools]}")
return tools
except Exception as e:
mcp_logger.error(f"列出工具失败: {e}", exc_info=True)
raise
@server.call_tool()
async def handle_call_tool(
name: str,
arguments: dict[str, Any] | None
) -> list[types.TextContent]:
"""
处理工具调用请求
Args:
name: 工具名称
arguments: 工具参数
Returns:
list[types.TextContent]: 工具执行结果（返回参数和对应的接口配置）
"""
try:
mcp_logger.info(f"收到工具调用请求: {name}")
mcp_logger.debug(f"工具参数: {arguments}")
# 获取查询配置缓存
queries = await get_queries_cache()
# 根据工具名称查找对应的 item（接口配置）
tool_item = None
for query in queries:
# tool_name = generate_tool_name(query['businessName'], query['id'])
tool_name = query['businessName']
if tool_name == name:
tool_item = query
break
# 构建返回结果
import json
if tool_item:
request_data = {
"datasourceId": tool_item.get("datasourceId"),
"businessName": tool_item.get("businessName"),
"businessDescription": tool_item.get("businessDescription"),
"sqlTemplate": tool_item.get("sqlTemplate"),
"parameters": tool_item.get("parameters"),
"testParams": arguments or {}
}
# 如果 arguments 中有 targetDatabaseName 且有值，添加到 request_data
if arguments and arguments.get("targetDatabaseName"):
request_data["targetDatabaseName"] = arguments["targetDatabaseName"]
mcp_logger.debug(f"添加目标数据库名称: {arguments['targetDatabaseName']}")
# 调用测试SQL API
try:
mcp_logger.info("正在调用测试SQL API...")
api_response = test_sql_with_schema(request_data)
mcp_logger.info("测试SQL API调用成功")
# 只返回 API 响应结果
result_text = json.dumps(api_response, ensure_ascii=False, indent=2)
except Exception as e:
error_msg = f"调用测试SQL API失败: {str(e)}"
mcp_logger.error(error_msg, exc_info=True)
result_text = json.dumps({"error": error_msg}, ensure_ascii=False, indent=2)
else:
error_msg = f"未找到工具 {name} 对应的配置"
result_text = json.dumps({"error": error_msg}, ensure_ascii=False, indent=2)
mcp_logger.debug(f"工具调用结果: {result_text}")
return [
types.TextContent(
type="text",
text=result_text
)
]
except Exception as e:
error_msg = f"工具调用失败: {name}, 错误: {e}"
mcp_logger.error(error_msg, exc_info=True)
return [
types.TextContent(
type="text",
text=f"错误: {error_msg}"
)
]
async def call_third_party_api(skill_id: str = None) -> list:
"""
调用第三方API获取技能信息并返回处理后的数据
Args:
skill_id: 技能ID（默认从环境变量 SKILL_ID 读取，如果未设置则使用 1981000305474482178）
Returns:
处理后的查询配置列表（businessQueries格式）
Example:
queries = await call_third_party_api()
# 返回: [{"id": "...", "businessName": "...", ...}, ...]
"""
try:
# 如果没有传入 skill_id，则从环境变量读取
if skill_id is None:
skill_id = get_skill_id()
mcp_logger.info(f"调用第三方API，skill_id: {skill_id}")
# 获取原始数据
raw_result = get_skill_by_id(skill_id)
mcp_logger.info(f"成功{raw_result}")
# 处理并返回
processed_queries = process_skill_response(raw_result)
mcp_logger.info(f"成功获取并处理 {len(processed_queries)} 条数据")
return processed_queries
except Exception as e:
mcp_logger.error(f"API调用失败: {e}", exc_info=True)
raise
async def async_main():
"""MCP 服务器异步主函数"""
try:
mcp_logger.info("=" * 60)
mcp_logger.info("正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor")
mcp_logger.info("版本: 0.1.0")
mcp_logger.info("=" * 60)
# 输出环境配置信息
env_config = get_env_config()
mcp_logger.info(f"环境配置 - Database ID: {env_config['database_id']}")
mcp_logger.info(f"环境配置 - Skill ID: {env_config['skill_id']}")
mcp_logger.info(f"环境配置 - Backend Base URL: {env_config['backend_base_url']}")
mcp_logger.info("=" * 60)
from mcp.server.stdio import stdio_server
async with stdio_server() as (read_stream, write_stream):
mcp_logger.info("MCP 服务器已启动，等待客户端连接...")
await server.run(
read_stream,
write_stream,
InitializationOptions(
server_name="lzwcai-mcp-sqlexecutor",
server_version="0.1.0",
capabilities=server.get_capabilities(
notification_options=NotificationOptions(),
experimental_capabilities={},
),
),
)
mcp_logger.info("MCP 服务器已关闭")
except Exception as e:
mcp_logger.error(f"MCP 服务器运行失败: {e}", exc_info=True)
raise
def main():
"""入口点函数（用于 console_scripts）"""
try:
# 初始化系统日志
# MCP协议使用stdio通信，必须禁用控制台输出以避免干扰JSON-RPC通信
logger_config.setup_logging(
app_name="lzwcai_mcp_sqlexecutor",
log_level=logging.INFO,
console_output=False # 禁用控制台输出
)
mcp_logger.info("开始运行 MCP SQL Executor 服务器")
asyncio.run(async_main())
except KeyboardInterrupt:
mcp_logger.info("收到中断信号，正在关闭服务器...")
except Exception as e:
mcp_logger.error(f"程序运行失败: {e}", exc_info=True)
raise
if __name__ == "__main__":
main()

35
lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml Normal file
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[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "lzwcai-mcp-sqlexecutor"
version = "0.1.6"
description = "MCP server for executing business SQL queries with dynamic tool generation"
readme = "README.md"
requires-python = ">=3.13"
license = {text = "MIT"}
authors = [
{name = "lzwcai", email = "your-email@example.com"},
]
keywords = ["mcp", "sql", "executor", "server"]
classifiers = [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.13",
]
dependencies = [
"httpx>=0.28.1",
"mcp[cli]>=1.10.1",
"pypinyin>=0.53.0",
]
[project.scripts]
lzwcai-mcp-sqlexecutor = "lzwcai_mcp_sqlexecutor.main:main"
[tool.hatch.build.targets.wheel]
packages = ["lzwcai_mcp_sqlexecutor"]
[tool.hatch.build.targets.wheel.force-include]
"lzwcai_mcp_sqlexecutor/businessQueries.json" = "lzwcai_mcp_sqlexecutor/businessQueries.json"

24
lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/__init__.py Normal file
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"""Utils package for lzwcai_mcp_sqlexecutor"""
from .json_helper import load_json
from .name_helper import generate_tool_name
from .schema_helper import generate_input_schema, validate_input_schema
from .api_client import DataSourceAPIClient, get_skill_by_id, process_skill_response, test_sql_with_schema
from .env_config import get_database_id, get_skill_id, get_backend_base_url, get_env_config, set_env_variable
__all__ = [
'load_json',
'generate_tool_name',
'generate_input_schema',
'validate_input_schema',
'DataSourceAPIClient',
'get_skill_by_id',
'process_skill_response',
'test_sql_with_schema',
'get_database_id',
'get_skill_id',
'get_backend_base_url',
'get_env_config',
'set_env_variable'
]

318
lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/api_client.py Normal file
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"""
第三方API调用客户端
用于调用外部数据源接口
"""
import httpx
import logging
import json
from typing import Dict, Any, Optional, List
# 支持直接运行和模块导入两种方式
try:
from .env_config import get_backend_base_url
except ImportError:
from env_config import get_backend_base_url
# 获取日志记录器
logger = logging.getLogger(__name__)
class DataSourceAPIClient:
"""数据源API客户端"""
def __init__(
self,
base_url: Optional[str] = None,
token: Optional[str] = None
):
"""
初始化API客户端
Args:
base_url: API基础URL（默认从环境变量 BACKEND_BASE_URL 读取，如果未设置则使用 http://192.168.2.236:8088）
token: 认证令牌（Bearer Token）
"""
# 如果没有传入 base_url，则从环境变量读取
if base_url is None:
base_url = get_backend_base_url()
self.base_url = base_url.rstrip('/')
self.token = token or "eyJhbGciOiJIUzUxMiJ9.eyJsb2dpbl91c2VyX2tleSI6IjJiYTk4ODllLWM2ZGItNDQ5YS1iZmFjLTQ2YzMxODFlODg5NCJ9.dvi8zm0LsWvJ_h9zD5blnHFRxa4z4_WBm1R487ekE7HlHzrN6dnvqhK8askqT5b1EcE8myHwRzLVMoI8UOjOrw"
self.client = httpx.Client(timeout=30.0)
def _get_headers(self) -> Dict[str, str]:
"""
获取请求头
Returns:
请求头字典
"""
return {
'Authorization': f'Bearer {self.token}',
}
def get_skill_by_id(self, skill_id: str) -> Dict[str, Any]:
"""
根据技能ID获取技能信息
Args:
skill_id: 技能ID
Returns:
API响应数据
Raises:
Exception: 请求失败时抛出
"""
try:
url = f"{self.base_url}/datasource/skill/getBySkillId/{skill_id}"
logger.info(f"正在调用API: {url}")
logger.info(f"请求参数 - skill_id: {skill_id}")
response = self.client.get(
url,
headers=self._get_headers()
)
# 检查HTTP状态码
response.raise_for_status()
# 解析JSON响应
data = response.json()
logger.info(f"API调用成功: {url}")
logger.debug(f"响应数据: {data}")
return data
except httpx.TimeoutException:
error_msg = f"API请求超时: {url}"
logger.error(error_msg)
raise Exception(error_msg)
except httpx.HTTPStatusError as e:
error_msg = f"API请求失败 (HTTP {e.response.status_code}): {url}"
logger.error(error_msg)
logger.error(f"错误响应: {e.response.text}")
raise Exception(error_msg)
except httpx.RequestError as e:
error_msg = f"API请求异常: {url}, 错误: {str(e)}"
logger.error(error_msg)
raise Exception(error_msg)
except Exception as e:
error_msg = f"处理API响应时出错: {str(e)}"
logger.error(error_msg, exc_info=True)
raise Exception(error_msg)
def test_sql_with_schema(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
"""
测试SQL语句并返回执行结果
Args:
request_data: 请求数据，包含以下字段：
- datasourceId: 数据源ID
- businessName: 业务名称
- businessDescription: 业务描述
- sqlTemplate: SQL模板
- parameters: 参数定义
- testParams: 测试参数
Returns:
API响应数据
Raises:
Exception: 请求失败时抛出
"""
try:
url = f"{self.base_url}/datasource/sqlExecutionLog/testSqlWithSchema"
# 构建请求头（包含Content-Type）
headers = self._get_headers()
headers['Content-Type'] = 'application/json'
headers['Accept'] = '*/*'
logger.info(f"正在调用测试SQL API: {url}")
logger.info(f"请求参数: {json.dumps(request_data, ensure_ascii=False, indent=2)}")
# 发送POST请求
response = self.client.post(
url,
headers=headers,
json=request_data
)
# 检查HTTP状态码
response.raise_for_status()
# 解析JSON响应
result = response.json()
logger.info(f"测试SQL API调用成功")
logger.debug(f"响应数据: {json.dumps(result, ensure_ascii=False, indent=2)}")
# 处理返回数据结构: {code, data: {errorMessage, data}, msg}
# 检查外层 code
if result.get("code") != 200:
error_msg = result.get("msg", "接口返回错误")
logger.error(f"接口返回错误: code={result.get('code')}, msg={error_msg}")
raise Exception(error_msg)
# 检查内层 errorMessage
inner_data = result.get("data", {})
if inner_data.get("errorMessage"):
error_msg = inner_data.get("errorMessage")
logger.error(f"接口业务错误: {error_msg}")
raise Exception(error_msg)
# 返回 data.data
return inner_data.get("data")
except httpx.TimeoutException:
error_msg = f"测试SQL API请求超时: {url}"
logger.error(error_msg)
raise Exception(error_msg)
except httpx.HTTPStatusError as e:
error_msg = f"测试SQL API请求失败 (HTTP {e.response.status_code}): {url}"
logger.error(error_msg)
logger.error(f"错误响应: {e.response.text}")
raise Exception(error_msg)
except httpx.RequestError as e:
error_msg = f"测试SQL API请求异常: {url}, 错误: {str(e)}"
logger.error(error_msg)
raise Exception(error_msg)
except Exception as e:
error_msg = f"处理测试SQL API响应时出错: {str(e)}"
logger.error(error_msg, exc_info=True)
raise Exception(error_msg)
def close(self):
"""关闭HTTP客户端"""
self.client.close()
# 创建默认客户端实例
default_client = DataSourceAPIClient()
def get_skill_by_id(skill_id: str, base_url: Optional[str] = None, token: Optional[str] = None) -> Dict[str, Any]:
"""
便捷函数：根据技能ID获取技能信息
Args:
skill_id: 技能ID
base_url: API基础URL（可选，默认从环境变量 BACKEND_BASE_URL 读取）
token: 认证令牌（可选，使用默认值）
Returns:
API响应数据
"""
if base_url or token:
client = DataSourceAPIClient(
base_url=base_url,
token=token
)
return client.get_skill_by_id(skill_id)
else:
return default_client.get_skill_by_id(skill_id)
def test_sql_with_schema(request_data: Dict[str, Any], base_url: Optional[str] = None, token: Optional[str] = None) -> Dict[str, Any]:
"""
便捷函数：测试SQL语句并返回执行结果
Args:
request_data: 请求数据，包含以下字段：
- datasourceId: 数据源ID
- businessName: 业务名称
- businessDescription: 业务描述
- sqlTemplate: SQL模板
- parameters: 参数定义
- testParams: 测试参数
base_url: API基础URL（可选，默认从环境变量 BACKEND_BASE_URL 读取）
token: 认证令牌（可选，使用默认值）
Returns:
API响应数据
"""
if base_url or token:
client = DataSourceAPIClient(
base_url=base_url,
token=token
)
return client.test_sql_with_schema(request_data)
else:
return default_client.test_sql_with_schema(request_data)
def process_skill_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
处理API响应数据，映射为businessQueries格式
Args:
response: API原始响应数据
Returns:
处理后的查询配置列表
"""
try:
# 提取data数组
data_list = response.get("data", [])
# 默认的员工ID参数schema
default_employee_schema = {
"type": "object",
"required": ["employeeId"],
"properties": {
"employeeId": {
"type": "number",
"description": "员工ID，用于标识员工的唯一数字标识符",
"examples": [1001, 2002]
}
}
}
# 映射每个skill为businessQuery格式
queries = []
for skill in data_list:
# 解析sqlParams字符串为JSON对象
sql_params = json.loads(skill.get("sqlParams", "{}"))
# 判断sqlParams是否为空对象
is_empty_params = (
not sql_params.get("properties") or
len(sql_params.get("properties", {})) == 0
) and (
not sql_params.get("required") or
len(sql_params.get("required", [])) == 0
)
# 如果是空对象，使用默认的员工ID参数
if is_empty_params:
logger.info(f"技能 {skill.get('name')} (ID: {skill.get('id')}) 的sqlParams为空，使用默认员工ID参数")
sql_params = default_employee_schema
# 映射字段
query = {
"id": skill.get("id"),
"businessName": skill.get("name"),
"businessDescription": skill.get("description"),
"sqlTemplate": skill.get("sqlTemplate"),
"parameters": sql_params,
"datasourceId": skill.get("datasourceId")
}
queries.append(query)
logger.info(f"成功处理 {len(queries)} 条技能数据")
return queries
except Exception as e:
logger.error(f"处理API响应数据失败: {e}", exc_info=True)
raise

88
lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/env_config.py Normal file
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"""环境变量配置模块"""
import os
from typing import Optional
def get_database_id(default: str = "29") -> str:
"""
获取数据库ID环境变量
Args:
default: 默认值（默认为 "29"）
Returns:
str: 数据库ID
Environment Variables:
databaseId: 数据库ID
"""
return os.environ.get("databaseId", default)
def get_skill_id(default: str = "") -> str:
"""
获取技能ID环境变量
Args:
default: 默认值（默认为 ""）
Returns:
str: 技能ID
Environment Variables:
skillId: 技能ID
"""
return os.environ.get("skillId", default)
def get_backend_base_url(default: str = "http://lzwcai-demp-corp-manager:8086") -> str:
"""
获取后端API基础URL环境变量
Args:
default: 默认值（默认为 "http://lzwcai-demp-corp-manager:8086"）
Returns:
str: 后端API基础URL
Environment Variables:
backendBaseUrl: 后端API基础URL
"""
return os.environ.get("backendBaseUrl", default)
def get_env_config() -> dict:
"""
获取所有环境配置
Returns:
dict: 包含所有配置的字典
Example:
config = get_env_config()
print(config['database_id']) # 输出: "29"
print(config['skill_id']) # 输出: ""
print(config['backend_base_url']) # 输出: "http://lzwcai-demp-corp-manager:8086"
"""
return {
"database_id": get_database_id(),
"skill_id": get_skill_id(),
"backend_base_url": get_backend_base_url()
}
def set_env_variable(key: str, value: str) -> None:
"""
设置环境变量（仅在当前进程中有效）
Args:
key: 环境变量名
value: 环境变量值
Example:
set_env_variable("databaseId", "30")
set_env_variable("skillId", "1234567890")
"""
os.environ[key] = value

60
lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/json_helper.py Normal file
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"""JSON 文件读取工具"""
import json
from pathlib import Path
from typing import Any, Union
def load_json(json_path: Union[str, Path]) -> Any:
"""
读取 JSON 文件并返回其内容
Args:
json_path: JSON 文件的路径（支持字符串或 Path 对象）
Returns:
JSON 文件中解析后的数据（可以是字典、列表或其他 JSON 类型）
Raises:
FileNotFoundError: 当文件不存在时
json.JSONDecodeError: 当 JSON 格式无效时
Exception: 其他读取错误
Example:
>>> data = load_json('config.json')
>>> print(data)
{'key': 'value'}
>>> data = load_json(Path('data/users.json'))
>>> print(data)
[{'id': 1, 'name': 'Alice'}]
"""
try:
# 转换为 Path 对象
path = Path(json_path)
# 检查文件是否存在
if not path.exists():
raise FileNotFoundError(f"JSON 文件不存在: {json_path}")
# 检查是否为文件
if not path.is_file():
raise ValueError(f"路径不是一个文件: {json_path}")
# 读取并解析 JSON 文件
with open(path, 'r', encoding='utf-8') as f:
data = json.load(f)
return data
except json.JSONDecodeError as e:
raise json.JSONDecodeError(
f"JSON 格式错误: {e.msg}",
e.doc,
e.pos
)
except FileNotFoundError:
raise
except Exception as e:
raise Exception(f"读取 JSON 文件时发生错误: {str(e)}")

489
lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/logger_config.py Normal file
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# -*- coding: utf-8 -*-
"""
统一日志配置模块
提供系统级别的日志配置和管理
"""
import os
import sys
import logging
from logging.handlers import RotatingFileHandler, TimedRotatingFileHandler
from datetime import datetime
from pathlib import Path
class LoggerConfig:
"""日志配置管理类"""
def __init__(self, logs_dir: str = None):
"""初始化日志配置
Args:
logs_dir: 日志目录路径，默认为项目根目录下的logs文件夹
"""
# 确定日志目录
if logs_dir:
self.logs_dir = Path(logs_dir)
else:
# 获取项目根目录（logger_config.py 在 utils 目录下，需要上升两层到达项目根目录）
project_root = Path(__file__).parent.parent
self.logs_dir = project_root / "logs"
# 创建日志目录
self.logs_dir.mkdir(exist_ok=True)
# 日志格式
self.log_format = '%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s'
self.date_format = '%Y-%m-%d %H:%M:%S'
# 从环境变量获取日志级别，默认为INFO
self.log_level = self._get_log_level_from_env()
# 是否已初始化
self._initialized = False
def _get_log_level_from_env(self) -> int:
"""从环境变量获取日志级别
Returns:
int: 日志级别
"""
log_level_str = os.getenv('LOG_LEVEL', 'INFO').upper()
# 日志级别映射
level_mapping = {
'DEBUG': logging.DEBUG,
'INFO': logging.INFO,
'WARNING': logging.WARNING,
'WARN': logging.WARNING, # 兼容性别名
'ERROR': logging.ERROR,
'CRITICAL': logging.CRITICAL,
'FATAL': logging.CRITICAL # 兼容性别名
}
return level_mapping.get(log_level_str, logging.INFO)
def setup_logging(self,
app_name: str = "lzwcai_mcp_sqlexecutor",
log_level: int = logging.INFO,
max_file_size: int = 10 * 1024 * 1024, # 10MB
backup_count: int = 5,
console_output: bool = True) -> logging.Logger:
"""设置系统日志配置
Args:
app_name: 应用名称，用于日志文件命名
log_level: 日志级别
max_file_size: 单个日志文件最大大小（字节）
backup_count: 保留的备份文件数量
console_output: 是否输出到控制台
Returns:
logging.Logger: 配置好的根日志器
"""
if self._initialized:
return logging.getLogger()
# 设置根日志器
root_logger = logging.getLogger()
root_logger.setLevel(log_level)
# 清除现有的处理器
for handler in root_logger.handlers[:]:
root_logger.removeHandler(handler)
# 创建格式化器
formatter = logging.Formatter(self.log_format, self.date_format)
# 1. 主日志文件 - 按大小滚动
main_log_file = self.logs_dir / f"{app_name}.log"
file_handler = RotatingFileHandler(
main_log_file,
maxBytes=max_file_size,
backupCount=backup_count,
encoding='utf-8'
)
file_handler.setLevel(log_level)
file_handler.setFormatter(formatter)
root_logger.addHandler(file_handler)
# 2. 错误日志文件 - 只记录ERROR及以上级别
error_log_file = self.logs_dir / f"{app_name}_error.log"
error_handler = RotatingFileHandler(
error_log_file,
maxBytes=max_file_size,
backupCount=backup_count,
encoding='utf-8'
)
error_handler.setLevel(logging.ERROR)
error_handler.setFormatter(formatter)
root_logger.addHandler(error_handler)
# 3. 按日期滚动的日志文件
daily_log_file = self.logs_dir / f"{app_name}_daily.log"
daily_handler = TimedRotatingFileHandler(
daily_log_file,
when='midnight',
interval=1,
backupCount=30, # 保留30天
encoding='utf-8'
)
daily_handler.setLevel(log_level)
daily_handler.setFormatter(formatter)
daily_handler.suffix = "%Y-%m-%d"
root_logger.addHandler(daily_handler)
# 4. 控制台输出
# 重要：MCP协议使用stdio时，必须将日志输出到stderr，stdout仅用于JSON-RPC通信
if console_output:
console_handler = logging.StreamHandler(sys.stderr)
console_handler.setLevel(log_level)
console_formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s',
self.date_format
)
console_handler.setFormatter(console_formatter)
root_logger.addHandler(console_handler)
self._initialized = True
# 记录初始化信息
root_logger.info(f"日志系统初始化完成 - 日志目录: {self.logs_dir}")
root_logger.info(f"日志配置 - 级别: {logging.getLevelName(log_level)}, 文件大小限制: {max_file_size//1024//1024}MB, 备份数量: {backup_count}")
return root_logger
def get_module_logger(self, module_name: str) -> logging.Logger:
"""获取模块专用日志器
Args:
module_name: 模块名称
Returns:
logging.Logger: 模块日志器
"""
return logging.getLogger(module_name)
def create_component_logger(self,
component_name: str,
log_file: str = None,
log_level: int = None) -> logging.Logger:
"""为特定组件创建独立的日志器
Args:
component_name: 组件名称
log_file: 独立日志文件名（可选）
log_level: 日志级别（可选）
Returns:
logging.Logger: 组件日志器
"""
logger = logging.getLogger(component_name)
if log_file:
# 为组件创建独立的日志文件
component_log_file = self.logs_dir / log_file
handler = RotatingFileHandler(
component_log_file,
maxBytes=5 * 1024 * 1024, # 5MB
backupCount=3,
encoding='utf-8'
)
formatter = logging.Formatter(self.log_format, self.date_format)
handler.setFormatter(formatter)
if log_level:
handler.setLevel(log_level)
logger.addHandler(handler)
logger.info(f"组件日志器创建完成: {component_name} -> {component_log_file}")
return logger
def setup_mqtt_logging(self) -> logging.Logger:
"""设置MQTT专用日志
Returns:
logging.Logger: MQTT日志器
"""
return self.create_component_logger(
"mqtt_communication",
"mqtt_communication.log",
logging.DEBUG
)
def setup_mcp_logging(self) -> logging.Logger:
"""设置MCP专用日志
Returns:
logging.Logger: MCP日志器
"""
return self.create_component_logger(
"mcp_services",
"mcp_services.log",
logging.DEBUG
)
def setup_api_logging(self) -> logging.Logger:
"""设置API专用日志
Returns:
logging.Logger: API日志器
"""
return self.create_component_logger(
"api_requests",
"api_requests.log",
logging.INFO
)
def get_logs_info(self) -> dict:
"""获取日志系统信息
Returns:
dict: 日志系统信息
"""
log_files = []
if self.logs_dir.exists():
for log_file in self.logs_dir.glob("*.log*"):
stat = log_file.stat()
log_files.append({
"name": log_file.name,
"size": stat.st_size,
"modified": datetime.fromtimestamp(stat.st_mtime).isoformat()
})
return {
"logs_directory": str(self.logs_dir),
"initialized": self._initialized,
"log_files": log_files,
"total_files": len(log_files)
}
def cleanup_old_logs(self, days: int = 30):
"""清理旧日志文件
Args:
days: 保留天数
"""
if not self.logs_dir.exists():
return
from datetime import timedelta
cutoff_time = datetime.now() - timedelta(days=days)
cleaned_files = []
for log_file in self.logs_dir.glob("*.log*"):
if log_file.stat().st_mtime < cutoff_time.timestamp():
try:
log_file.unlink()
cleaned_files.append(log_file.name)
except Exception as e:
logging.error(f"清理日志文件失败: {log_file.name}, 错误: {e}")
if cleaned_files:
logging.info(f"清理了 {len(cleaned_files)} 个旧日志文件: {cleaned_files}")
def set_log_level(self, level: int, logger_name: str = None):
"""动态调整日志级别
Args:
level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
logger_name: 指定日志器名称，None表示调整根日志器
"""
if logger_name:
logger = logging.getLogger(logger_name)
else:
logger = logging.getLogger()
old_level = logger.level
logger.setLevel(level)
# 同时调整所有处理器的级别
for handler in logger.handlers:
if not isinstance(handler, logging.StreamHandler) or handler.stream not in (sys.stdout, sys.stderr):
# 不调整控制台处理器的级别，保持原有设置
handler.setLevel(level)
level_name = logging.getLevelName(level)
old_level_name = logging.getLevelName(old_level)
target = logger_name or "根日志器"
logger.info(f"日志级别已调整: {target} {old_level_name} -> {level_name}")
def set_temporary_log_level(self, level: int, logger_name: str = None):
"""临时调整日志级别（会保存原始级别用于恢复）
Args:
level: 临时日志级别
logger_name: 指定日志器名称，None表示调整根日志器
"""
if not hasattr(self, '_original_levels'):
self._original_levels = {}
target_name = logger_name or 'root'
if logger_name:
logger = logging.getLogger(logger_name)
else:
logger = logging.getLogger()
# 保存原始级别
if target_name not in self._original_levels:
self._original_levels[target_name] = logger.level
# 设置新级别
self.set_log_level(level, logger_name)
level_name = logging.getLevelName(level)
target = logger_name or "根日志器"
logger.info(f"临时调整日志级别: {target} -> {level_name} (可通过restore_log_level恢复)")
def restore_log_level(self, logger_name: str = None):
"""恢复日志级别到调整前的状态
Args:
logger_name: 指定日志器名称，None表示恢复根日志器
"""
if not hasattr(self, '_original_levels'):
logging.warning("没有找到保存的原始日志级别")
return
target_name = logger_name or 'root'
if target_name not in self._original_levels:
logging.warning(f"没有找到 {target_name} 的原始日志级别")
return
original_level = self._original_levels[target_name]
self.set_log_level(original_level, logger_name)
# 清除保存的级别
del self._original_levels[target_name]
target = logger_name or "根日志器"
level_name = logging.getLevelName(original_level)
logging.info(f"已恢复日志级别: {target} -> {level_name}")
def get_current_log_levels(self) -> dict:
"""获取当前所有日志器的级别信息
Returns:
dict: 日志器级别信息
"""
levels_info = {}
# 根日志器
root_logger = logging.getLogger()
levels_info['root'] = {
'level': root_logger.level,
'level_name': logging.getLevelName(root_logger.level),
'handlers_count': len(root_logger.handlers)
}
# 其他已创建的日志器
for name, logger in logging.Logger.manager.loggerDict.items():
if isinstance(logger, logging.Logger):
levels_info[name] = {
'level': logger.level,
'level_name': logging.getLevelName(logger.level),
'handlers_count': len(logger.handlers)
}
return levels_info
# 全局日志配置实例
logger_config = LoggerConfig()
def setup_system_logging(app_name: str = "lzwcai_mcp_sqlexecutor",
log_level: int = logging.INFO) -> logging.Logger:
"""系统日志初始化快捷函数
Args:
app_name: 应用名称
log_level: 日志级别
Returns:
logging.Logger: 根日志器
"""
return logger_config.setup_logging(app_name, log_level)
def get_logger(name: str) -> logging.Logger:
"""获取日志器的快捷函数
Args:
name: 日志器名称
Returns:
logging.Logger: 日志器实例
"""
return logger_config.get_module_logger(name)
def set_log_level(level: int, logger_name: str = None):
"""动态调整日志级别的快捷函数
Args:
level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
logger_name: 指定日志器名称，None表示调整根日志器
Examples:
# 调整根日志器为DEBUG级别
set_log_level(logging.DEBUG)
# 调整特定模块日志器为WARNING级别
set_log_level(logging.WARNING, "agent_ontology.core")
"""
logger_config.set_log_level(level, logger_name)
def set_temporary_log_level(level: int, logger_name: str = None):
"""临时调整日志级别的快捷函数
Args:
level: 临时日志级别
logger_name: 指定日志器名称，None表示调整根日志器
Examples:
# 临时调整为DEBUG级别进行调试
set_temporary_log_level(logging.DEBUG)
# ... 进行调试 ...
# 恢复原始级别
restore_log_level()
"""
logger_config.set_temporary_log_level(level, logger_name)
def restore_log_level(logger_name: str = None):
"""恢复日志级别的快捷函数
Args:
logger_name: 指定日志器名称，None表示恢复根日志器
"""
logger_config.restore_log_level(logger_name)
def get_current_log_levels() -> dict:
"""获取当前日志级别信息的快捷函数
Returns:
dict: 日志器级别信息
Examples:
levels = get_current_log_levels()
print(f"根日志器级别: {levels['root']['level_name']}")
"""
return logger_config.get_current_log_levels()
# 便捷的日志级别常量
class LogLevel:
"""日志级别常量类"""
DEBUG = logging.DEBUG
INFO = logging.INFO
WARNING = logging.WARNING
ERROR = logging.ERROR
CRITICAL = logging.CRITICAL

41
lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/name_helper.py Normal file
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# -*- coding: utf-8 -*-
"""
名称生成工具模块
"""
from pypinyin import lazy_pinyin, Style
import logging
logger = logging.getLogger(__name__)
def generate_tool_name(business_name: str, tool_id: str) -> str:
"""
根据业务名称和ID生成工具名称
格式: tool_拼音_id
Args:
business_name: 业务名称（中文）
tool_id: 工具ID
Returns:
str: 格式化的工具名称
"""
try:
# 将中文转换为拼音（无音调，小写）
pinyin_list = lazy_pinyin(business_name, style=Style.NORMAL)
# 拼接拼音
pinyin_str = ''.join(pinyin_list)
# 将 ID 中的 '-' 替换为 '_'
formatted_id = tool_id.replace('-', '_')
# 组合成最终的工具名称
tool_name = f"tool_{pinyin_str}_{formatted_id}"
return tool_name
except Exception as e:
logger.error(f"生成工具名称失败: {business_name}, {tool_id}, 错误: {e}", exc_info=True)
# 降级处理：如果拼音转换失败，使用 ID
return f"tool_{tool_id.replace('-', '_')}"

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lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/schema_helper.py Normal file
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# -*- coding: utf-8 -*-
"""
Schema 生成工具模块
"""
from typing import Any, Dict, List
def generate_input_schema(parameters: Dict[str, Any]) -> Dict[str, Any]:
"""
从查询配置的参数定义生成 MCP 工具的 inputSchema
此函数会保留完整的 JSON Schema 信息，包括：
- type: Schema 类型（通常是 "object"）
- required: 必填字段列表
- properties: 属性定义（包括每个属性的 type, description, format, examples 等）
- description: Schema 的整体描述（如果有）
- 以及其他任何 JSON Schema 标准字段
此函数还会自动添加以下字段（如果原始 parameters 中未定义）：
- targetDatabaseName: 目标数据库名称（非必填，默认为空字符串）
Args:
parameters: 查询配置中的参数定义字典，应该是一个完整的 JSON Schema 对象
Returns:
Dict[str, Any]: 符合 JSON Schema 规范的 inputSchema 对象
Example:
>>> params = {
... "type": "object",
... "required": ["userId", "startTime"],
... "properties": {
... "userId": {
... "type": "integer",
... "description": "用户的唯一标识符",
... "examples": [10086]
... },
... "startTime": {
... "type": "string",
... "format": "date-time",
... "description": "查询的起始时间",
... "examples": ["2023-01-01 00:00:00"]
... }
... }
... }
>>> schema = generate_input_schema(params)
>>> # schema 将包含所有原始信息，包括 format 和 examples
>>> # 同时会自动添加 targetDatabaseName 字段
"""
# 如果 parameters 本身就是一个完整的 JSON Schema 对象，直接使用
# 但确保至少包含 type 和 properties
if not parameters:
# 如果 parameters 为空，返回一个空的 object schema
return {
"type": "object",
"properties": {},
"required": []
}
# 检查 parameters 是否已经是完整的 JSON Schema 格式
# 完整格式应该有 "type": "object" 和 "properties" 字段
if "type" in parameters and parameters.get("type") == "object" and "properties" in parameters:
# 已经是完整的 JSON Schema 格式，直接使用
input_schema = dict(parameters)
else:
# parameters 是简化格式（直接是参数定义），需要转换为 JSON Schema 格式
# 例如: {"workOrderNumber": {"type": "string", "description": "...", "required": true}}
properties = {}
required = []
for param_name, param_def in parameters.items():
if isinstance(param_def, dict):
# 提取 required 标记
if param_def.get("required", False):
required.append(param_name)
# 复制参数定义，但移除 required 字段（它应该在顶层的 required 数组中）
prop_def = {k: v for k, v in param_def.items() if k != "required"}
properties[param_name] = prop_def
input_schema = {
"type": "object",
"properties": properties,
"required": required
}
# 确保必需的字段存在
if "type" not in input_schema:
input_schema["type"] = "object"
if "properties" not in input_schema:
input_schema["properties"] = {}
if "required" not in input_schema:
input_schema["required"] = []
# 添加 targetDatabaseName 字段（如果不存在）
if "targetDatabaseName" not in input_schema["properties"]:
input_schema["properties"]["targetDatabaseName"] = {
"type": "string",
"description": "目标数据库名称",
"default": ""
}
# 保留所有其他字段，如 description, examples, format 等
# JSON Schema 标准支持的字段都会被保留：
# - additionalProperties
# - patternProperties
# - minProperties / maxProperties
# - dependencies
# - 等等
return input_schema
def validate_input_schema(schema: Dict[str, Any]) -> tuple[bool, str]:
"""
验证 inputSchema 是否符合基本的 JSON Schema 规范
Args:
schema: 要验证的 schema 对象
Returns:
tuple[bool, str]: (是否有效, 错误消息或成功消息)
Example:
>>> schema = {"type": "object", "properties": {"id": {"type": "string"}}}
>>> is_valid, msg = validate_input_schema(schema)
>>> print(is_valid, msg)
True, "Schema 验证通过"
"""
if not isinstance(schema, dict):
return False, "Schema 必须是一个字典对象"
if schema.get("type") != "object":
return False, "Schema 的 type 字段必须是 'object'"
if "properties" not in schema:
return False, "Schema 必须包含 properties 字段"
if not isinstance(schema.get("properties"), dict):
return False, "Schema 的 properties 字段必须是一个字典对象"
# 验证 required 字段（如果存在）
if "required" in schema:
required = schema["required"]
if not isinstance(required, list):
return False, "Schema 的 required 字段必须是一个列表"
# 验证所有 required 的字段都在 properties 中定义
properties = schema["properties"]
for field in required:
if field not in properties:
return False, f"必填字段 '{field}' 未在 properties 中定义"
# 验证 properties 中每个字段的定义
for prop_name, prop_def in schema["properties"].items():
if not isinstance(prop_def, dict):
return False, f"属性 '{prop_name}' 的定义必须是一个字典对象"
if "type" not in prop_def:
return False, f"属性 '{prop_name}' 必须包含 type 字段"
return True, "Schema 验证通过"

497
lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/uv.lock generated Normal file
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12
lzwcai_mcpskills_analyzeWorkOrder/main.py Normal file
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"""
Entry point for lzwcai-mcp-sqlexecutor
Runs the MCP server for SQL query execution
"""
import os
os.environ["databaseId"] = "19"
os.environ["backendBaseUrl"] = "http://192.168.11.24:8088"
if __name__ == "__main__":
# Import and run the actual MCP server
from lzwcai_mcpskills_analyzeWorkOrder.main import main
main()

35
lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml Normal file
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[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "lzwcai-mcpskills-analyzeWorkOrder"
version = "0.1.8"
description = "MCP server for executing business SQL queries with dynamic tool generation"
readme = "README.md"
requires-python = ">=3.13"
license = {text = "MIT"}
authors = [
{name = "lzwcai", email = "your-email@example.com"},
]
keywords = ["mcp", "sql", "executor", "server"]
classifiers = [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.13",
]
dependencies = [
"httpx>=0.28.1",
"mcp[cli]>=1.10.1",
"pypinyin>=0.53.0",
]
[project.scripts]
lzwcai-mcpskills-analyzeWorkOrder = "lzwcai_mcpskills_analyzeWorkOrder.main:main"
[tool.hatch.build.targets.wheel]
packages = ["lzwcai_mcpskills_analyzeWorkOrder"]
[tool.hatch.build.targets.wheel.force-include]
"lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json" = "lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json"

80
tools_config.json Normal file
View File

@@ -0,0 +1,80 @@
[
{
"id": "2006300000000000000",
"createBy": "admin",
"createTime": "2026-01-02 10:00:00",
"updateBy": null,
"updateTime": null,
"serviceId": "2005848885546889218",
"uniqueName": "查询列表",
"name": "chaxunliebiao_x1y2z3w4",
"description": "查询所有工单列表，返回fact_work_order表的全部数据",
"visualizable": 1,
"toolPrompt": "查询所有工单列表数据",
"toolType": "sql",
"datasourceId": "16",
"sqlTemplate": "SELECT * FROM fact_work_order",
"sqlParams": "{\"type\":\"object\",\"required\":[],\"properties\":{}}",
"resultType": "list",
"sourceType": "manual",
"trainingTaskId": null,
"tableMetadataIds": "",
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"outputJsonSchema": "",
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{
"id": "2006300000000000004",
"createBy": "admin",
"createTime": "2026-01-02 10:00:00",
"updateBy": null,
"updateTime": null,
"serviceId": "2005848885546889218",
"uniqueName": "工单详情",
"name": "gongdanxiangqing_m3n4o5p6",
"description": "根据工单编号查询工单详情，包括工单基本信息、产品信息、委外供应商、工艺路线等",
"visualizable": 1,
"toolPrompt": "输入工单编号查询工单详情，返回工单状态、数量、产品信息、供应商、工艺路线等",
"toolType": "sql",
"datasourceId": "16",
"sqlTemplate": "SELECT wo.work_order_id, wo.work_order_number AS 工单编号, wo.status AS 工单状态, wo.planned_qty AS 计划数量, wo.completed_qty AS 完成数量, wo.operation_good_qty AS 工序良品数, wo.operation_bad_qty AS 工序不良品数, wo.qc_good_qty AS 质检良品数, wo.qc_bad_qty AS 质检不良品数, wo.source_doc_number AS 来源单据编号, wo.event_time_utc AS 事件时间, p.product_code AS 产品编码, p.product_name AS 产品名称, p.product_spec AS 产品规格, s.supplier_name AS 委外供应商, r.routing_name AS 工艺路线 FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = true LEFT JOIN dim_supplier s ON wo.supplier_id = s.supplier_id AND s.is_current = true LEFT JOIN dim_routing r ON wo.routing_id = r.routing_id AND r.is_current = true WHERE wo.work_order_number = {workOrderNumber}",
"sqlParams": "{\"type\":\"object\",\"required\":[\"workOrderNumber\"],\"properties\":{\"workOrderNumber\":{\"type\":\"string\",\"description\":\"工单编号\",\"examples\":[\"WO1311343859\"]}}}",
"resultType": "list",
"sourceType": "manual",
"trainingTaskId": null,
"tableMetadataIds": "",
"executionCount": 0,
"visualizationConfigs": null,
"inputJsonSchema": "",
"outputJsonSchema": "",
"lastExecutionTime": null
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{
"id": "2006300000000000005",
"createBy": "admin",
"createTime": "2026-01-02 10:00:00",
"updateBy": null,
"updateTime": null,
"serviceId": "2005848885546889218",
"uniqueName": "工单执行进度与异常节点看板",
"name": "gongdanzhixingjinduyuyichangjiediankanban_q7r8s9t0",
"description": "工单执行进度与异常节点看板，展示工单完工进度、工序良率、质检良率、异常标识等关键监控指标",
"visualizable": 1,
"toolPrompt": "查看工单执行进度与异常节点看板，展示完工进度百分比、工序状态、报工统计、良率分析和异常标识",
"toolType": "sql",
"datasourceId": "16",
"sqlTemplate": "WITH work_order_progress AS (SELECT wo.work_order_number, wo.status AS work_order_status, wo.planned_qty, wo.completed_qty, wo.operation_good_qty, wo.operation_bad_qty, wo.qc_good_qty, wo.qc_bad_qty, wo.source_doc_number AS sales_order_number, wo.event_time_utc, p.product_code, p.product_name, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 2) ELSE 0 END AS completion_pct, CASE WHEN (wo.operation_good_qty + wo.operation_bad_qty) > 0 THEN ROUND(wo.operation_good_qty * 100.0 / (wo.operation_good_qty + wo.operation_bad_qty), 2) ELSE NULL END AS operation_yield_pct, CASE WHEN (wo.qc_good_qty + wo.qc_bad_qty) > 0 THEN ROUND(wo.qc_good_qty * 100.0 / (wo.qc_good_qty + wo.qc_bad_qty), 2) ELSE NULL END AS qc_yield_pct FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = true), task_summary AS (SELECT work_order_number, COUNT(*) AS total_tasks, SUM(CASE WHEN actual_end_time_utc IS NOT NULL THEN 1 ELSE 0 END) AS completed_tasks, SUM(CASE WHEN actual_start_time_utc IS NOT NULL AND actual_end_time_utc IS NULL THEN 1 ELSE 0 END) AS in_progress_tasks, SUM(CASE WHEN actual_start_time_utc IS NULL THEN 1 ELSE 0 END) AS pending_tasks, SUM(bad_qty) AS total_bad_qty FROM fact_operation_task GROUP BY work_order_number), labor_summary AS (SELECT work_order_number, SUM(report_qty) AS total_report_qty, SUM(good_qty) AS total_good_qty, SUM(bad_qty) AS total_bad_qty, SUM(duration_minutes) AS total_work_minutes FROM fact_labor_report GROUP BY work_order_number), qc_summary AS (SELECT work_order_number, COUNT(*) AS qc_record_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection WHERE work_order_number IS NOT NULL GROUP BY work_order_number) SELECT wp.work_order_number AS 工单编号, wp.product_code AS 产品编码, wp.product_name AS 产品名称, wp.sales_order_number AS 关联销售订单, wp.work_order_status AS 工单状态, wp.planned_qty AS 计划数量, wp.completed_qty AS 完成数量, wp.completion_pct AS 完工进度百分比, COALESCE(ts.total_tasks, 0) AS 工序总数, COALESCE(ts.completed_tasks, 0) AS 已完成工序, COALESCE(ts.in_progress_tasks, 0) AS 进行中工序, COALESCE(ts.pending_tasks, 0) AS 待开始工序, COALESCE(ls.total_report_qty, 0) AS 累计报工数, COALESCE(ls.total_work_minutes, 0) AS 累计工时_分钟, wp.operation_yield_pct AS 工序良率, wp.qc_yield_pct AS 质检良率, COALESCE(qc.total_pass_qty, 0) AS 质检通过数, COALESCE(qc.total_fail_qty, 0) AS 质检不通过数, CASE WHEN wp.completion_pct < 50 AND wp.work_order_status = 'STARTED' THEN '进度滞后' WHEN wp.operation_yield_pct < 90 THEN '工序良率异常' WHEN wp.qc_yield_pct < 95 THEN '质检良率异常' WHEN ts.total_bad_qty > 0 THEN '存在不良品' ELSE '正常' END AS 异常标识, wp.event_time_utc AS 最后更新时间 FROM work_order_progress wp LEFT JOIN task_summary ts ON wp.work_order_number = ts.work_order_number LEFT JOIN labor_summary ls ON wp.work_order_number = ls.work_order_number LEFT JOIN qc_summary qc ON wp.work_order_number = qc.work_order_number ORDER BY CASE wp.work_order_status WHEN 'STARTED' THEN 1 WHEN 'RELEASED' THEN 2 WHEN 'PLANNED' THEN 3 ELSE 4 END, wp.event_time_utc DESC",
"sqlParams": "{\"type\":\"object\",\"required\":[],\"properties\":{}}",
"resultType": "list",
"sourceType": "manual",
"trainingTaskId": null,
"tableMetadataIds": "",
"executionCount": 0,
"visualizationConfigs": "{\"chartType\":\"table\",\"highlightRules\":[{\"field\":\"异常标识\",\"condition\":\"notEquals\",\"value\":\"正常\",\"style\":\"danger\"}]}",
"inputJsonSchema": "",
"outputJsonSchema": "",
"lastExecutionTime": null
}
]