feat(lzwcai-demp-tool-server-dify-to-mcp): 初始化 Dify 集成工具模块
新增 Dify 到 MCP 的集成工具，支持通过 Dify API 将模型部署到 MCP 平台并进行推理。 该模块包含完整的服务器实现、依赖配置和命令行启动脚本。 主要功能： - 支持 Workflow 和 Completion 模式的调用 - 自动翻译工具名称为驼峰命名格式 - 提供文件上传与任务停止接口 - 兼容流式与非流式响应处理
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lzwcai_mcp_sqlexecutor/README.md
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lzwcai_mcp_sqlexecutor/README.md
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# lzwcai-mcp-sqlexecutor
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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。
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## 功能特性
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- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具
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- 🔧 灵活配置：支持自定义业务查询和参数验证
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- 📝 完整日志：详细的操作日志记录
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- 🌐 中文支持：工具名称自动转换为拼音
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## 安装
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### 使用 pip 安装
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```bash
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pip install lzwcai-mcp-sqlexecutor
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```
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### 从源码安装
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```bash
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git clone <repository-url>
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cd lzwcai_mcp_sqlexecutor
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pip install -e .
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```
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### 使用 uv 安装（推荐）
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```bash
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uv pip install lzwcai-mcp-sqlexecutor
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```
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## 使用方法
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### 命令行启动
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安装后，可以直接通过命令启动：
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```bash
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lzwcai-mcp-sqlexecutor
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```
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### 作为 Python 模块运行
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```bash
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python -m lzwcai_mcp_sqlexecutor.main
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```
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### 配置到 MCP 客户端
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在你的 MCP 客户端配置文件中添加：
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```json
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{
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"mcpServers": {
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"lzwcai-sqlexecutor": {
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"command": "lzwcai-mcp-sqlexecutor"
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}
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}
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}
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```
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## 配置说明
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### businessQueries.json
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在 `businessQueries.json` 中定义你的业务查询：
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```json
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[
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{
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"id": "query-001",
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"businessName": "用户订单查询",
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"businessDescription": "根据用户ID查询订单信息",
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"sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}",
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"parameters": {
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"type": "object",
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"required": ["userId"],
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"properties": {
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"userId": {
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"type": "integer",
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"description": "用户的唯一标识符",
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"examples": [10086]
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}
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}
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}
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}
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]
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```
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## 开发
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### 依赖项
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- Python >= 3.13
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- httpx >= 0.28.1
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- mcp[cli] >= 1.10.1
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- pypinyin >= 0.53.0
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### 本地开发
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```bash
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# 克隆仓库
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git clone <repository-url>
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cd lzwcai_mcp_sqlexecutor
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# 安装开发依赖
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pip install -e .
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# 运行服务器
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python -m lzwcai_mcp_sqlexecutor.main
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```
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## 构建与发布
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### 使用 build 构建
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```bash
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pip install build
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python -m build
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```
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### 发布到 PyPI
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```bash
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pip install twine
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twine upload dist/*
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```
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## 许可证
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MIT License
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## 作者
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lzwcai
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10
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/.gitignore
vendored
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10
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/.gitignore
vendored
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# Python-generated files
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__pycache__/
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*.py[oc]
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build/
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dist/
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wheels/
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*.egg-info
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# Virtual environments
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.venv
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@@ -0,0 +1 @@
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3.13
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154
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/README.md
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154
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/README.md
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@@ -0,0 +1,154 @@
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# lzwcai-mcp-sqlexecutor
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|
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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
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||||
|
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- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具
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- 🔧 灵活配置：支持自定义业务查询和参数验证
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- 📝 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）
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- 🌐 中文支持：工具名称自动转换为拼音
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## 安装
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### 使用 pip 安装
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```bash
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pip install lzwcai-mcp-sqlexecutor
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```
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### 从源码安装
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```bash
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git clone <repository-url>
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cd lzwcai_mcp_sqlexecutor
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pip install -e .
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```
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### 使用 uv 安装（推荐）
|
||||
|
||||
```bash
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uv pip install lzwcai-mcp-sqlexecutor
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```
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## 使用方法
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||||
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### 命令行启动
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||||
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安装后，可以直接通过命令启动：
|
||||
|
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```bash
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lzwcai-mcp-sqlexecutor
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```
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### 作为 Python 模块运行
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```bash
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python -m lzwcai_mcp_sqlexecutor.main
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```
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### 配置到 MCP 客户端
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||||
|
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在你的 MCP 客户端配置文件中添加：
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"lzwcai-sqlexecutor": {
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"command": "lzwcai-mcp-sqlexecutor"
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}
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}
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}
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```
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## 配置说明
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||||
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### businessQueries.json
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在 `businessQueries.json` 中定义你的业务查询：
|
||||
|
||||
```json
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[
|
||||
{
|
||||
"id": "query-001",
|
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"businessName": "用户订单查询",
|
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"businessDescription": "根据用户ID查询订单信息",
|
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"sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}",
|
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"parameters": {
|
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"type": "object",
|
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"required": ["userId"],
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"properties": {
|
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"userId": {
|
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"type": "integer",
|
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"description": "用户的唯一标识符",
|
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"examples": [10086]
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}
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}
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}
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}
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]
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```
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## 开发
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### 依赖项
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||||
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- Python >= 3.13
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- httpx >= 0.28.1
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||||
- mcp[cli] >= 1.10.1
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- pypinyin >= 0.53.0
|
||||
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||||
### 本地开发
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||||
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||||
```bash
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# 克隆仓库
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git clone <repository-url>
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cd lzwcai_mcp_sqlexecutor
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# 安装开发依赖
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pip install -e .
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# 运行服务器
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python -m lzwcai_mcp_sqlexecutor.main
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```
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## 构建与发布
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||||
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### 使用 build 构建
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||||
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```bash
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pip install build
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python -m build
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```
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||||
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### 发布到 PyPI
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||||
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```bash
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pip install twine
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twine upload dist/*
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```
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## 常见问题
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### MCP Inspector 显示 JSON 解析错误
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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：
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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。
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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：
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- `lzwcai_mcp_sqlexecutor.log` - 主日志文件
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- `lzwcai_mcp_sqlexecutor_error.log` - 错误日志
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- `lzwcai_mcp_sqlexecutor_daily.log` - 按日期滚动的日志
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- `mcp_services.log` - MCP 服务专用日志
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3. **查看日志**：如果需要调试，请查看 `logs/` 目录下的日志文件。
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## 许可证
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MIT License
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## 作者
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lzwcai
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@@ -0,0 +1,9 @@
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"""
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lzwcai-mcp-sqlexecutor - MCP server for executing business SQL queries
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"""
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__version__ = "0.1.2"
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__author__ = "lzwcai"
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__all__ = []
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@@ -0,0 +1 @@
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[]
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@@ -0,0 +1,51 @@
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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
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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
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2025-10-23 15:05:51 - mcp_services - INFO - [main.py:341] - 开始运行 MCP SQL Executor 服务器
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2025-10-23 15:05:51 - mcp_services - INFO - [main.py:293] - ============================================================
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2025-10-23 15:05:51 - mcp_services - INFO - [main.py:294] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor
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2025-10-23 15:05:51 - mcp_services - INFO - [main.py:295] - 版本: 0.1.0
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2025-10-23 15:05:51 - mcp_services - INFO - [main.py:296] - ============================================================
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2025-10-23 15:05:51 - mcp_services - INFO - [main.py:300] - 环境配置 - Database ID: 29
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2025-10-23 15:05:51 - mcp_services - INFO - [main.py:301] - 环境配置 - Skill ID: 1981195682443014146
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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
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2025-10-23 15:05:51 - mcp_services - INFO - [main.py:303] - ============================================================
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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...
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||||
2025-10-23 15:05:52 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
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||||
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）...
|
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2025-10-23 15:05:52 - mcp_services - INFO - [main.py:272] - 调用第三方API，skill_id: 1981195682443014146
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||||
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
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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 "
|
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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
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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}
|
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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
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2025-10-23 15:05:52 - mcp_services - INFO - [main.py:282] - 成功获取并处理 0 条数据
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2025-10-23 15:05:52 - mcp_services - INFO - [main.py:128] - API配置: 0 条
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||||
2025-10-23 15:05:52 - mcp_services - INFO - [main.py:129] - API配置数组: []
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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
|
||||
@@ -0,0 +1,51 @@
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
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_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 - 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 - 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 - 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_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 - 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 - 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
|
||||
358
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/main.py
Normal file
358
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/main.py
Normal file
@@ -0,0 +1,358 @@
|
||||
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_API
|
||||
# ================================
|
||||
|
||||
|
||||
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_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/pyproject.toml
Normal file
35
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/pyproject.toml
Normal file
@@ -0,0 +1,35 @@
|
||||
[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"
|
||||
@@ -0,0 +1,24 @@
|
||||
"""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'
|
||||
]
|
||||
|
||||
@@ -0,0 +1,303 @@
|
||||
"""
|
||||
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.debug(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.debug(f"请求数据: {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
|
||||
data = response.json()
|
||||
|
||||
logger.info(f"测试SQL API调用成功")
|
||||
logger.debug(f"响应数据: {json.dumps(data, ensure_ascii=False, indent=2)}")
|
||||
|
||||
return 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
|
||||
|
||||
@@ -0,0 +1,88 @@
|
||||
"""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
|
||||
|
||||
@@ -0,0 +1,60 @@
|
||||
"""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)}")
|
||||
|
||||
@@ -0,0 +1,489 @@
|
||||
# -*- 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
|
||||
@@ -0,0 +1,41 @@
|
||||
# -*- 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('-', '_')}"
|
||||
|
||||
@@ -0,0 +1,142 @@
|
||||
# -*- 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": []
|
||||
}
|
||||
|
||||
# 深拷贝 parameters 以避免修改原始数据
|
||||
input_schema = dict(parameters)
|
||||
|
||||
# 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_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/uv.lock
generated
Normal file
497
lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/uv.lock
generated
Normal file
@@ -0,0 +1,497 @@
|
||||
version = 1
|
||||
revision = 2
|
||||
requires-python = ">=3.13"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/aff/07c09a53a08bc/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/1f0/2e8b43a8fbbc3/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.11.0"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "idna" },
|
||||
{ name = "sniffio" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/82a/8d0b81e318cc5/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/028/7e96f4d26d414/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "25.4.0"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/16d/5969b87f0859e/attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/adc/f7e2a1fb3b36a/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.10.5"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/47c/09d31ccf2acf0/certifi-2025.10.5.tar.gz", hash = "sha256:47c09d31ccf2acf0be3f701ea53595ee7e0b8fa08801c6624be771df09ae7b43" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/0f2/12c2744a9bb6d/certifi-2025.10.5-py3-none-any.whl", hash = "sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.3.0"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/e7b/8232224eba16f/click-8.3.0.tar.gz", hash = "sha256:e7b8232224eba16f4ebe410c25ced9f7875cb5f3263ffc93cc3e8da705e229c4" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/9b9/f285302c6e306/click-8.3.0-py3-none-any.whl", hash = "sha256:9b9f285302c6e3064f4330c05f05b81945b2a39544279343e6e7c5f27a9baddc" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
version = "0.4.6"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/086/95f5cb7ed6e05/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/4f1/d9991f5acc0ca/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.16.0"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/4e3/5b956cf45792e/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/63c/f8bbe7522de3b/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpcore"
|
||||
version = "1.0.9"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "certifi" },
|
||||
{ name = "h11" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/6e3/4463af53fd2ab/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/2d4/00746a40668fc/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpx"
|
||||
version = "0.28.1"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
{ name = "certifi" },
|
||||
{ name = "httpcore" },
|
||||
{ name = "idna" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/75e/98c5f16b0f35b/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/d90/9fcccc110f8c7/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpx-sse"
|
||||
version = "0.4.3"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/9b1/ed0127459a660/httpx_sse-0.4.3.tar.gz", hash = "sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/0ac/1c9fe3c0afad2/httpx_sse-0.4.3-py3-none-any.whl", hash = "sha256:0ac1c9fe3c0afad2e0ebb25a934a59f4c7823b60792691f779fad2c5568830fc" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
version = "3.11"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/795/dafcc9c04ed0c/idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/771/a87f49d9defaf/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jsonschema"
|
||||
version = "4.25.1"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "attrs" },
|
||||
{ name = "jsonschema-specifications" },
|
||||
{ name = "referencing" },
|
||||
{ name = "rpds-py" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/e4a/9655ce0da0c0b/jsonschema-4.25.1.tar.gz", hash = "sha256:e4a9655ce0da0c0b67a085847e00a3a51449e1157f4f75e9fb5aa545e122eb85" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/3fb/a0169e345c717/jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jsonschema-specifications"
|
||||
version = "2025.9.1"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "referencing" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/b54/0987f239e7456/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/988/02fee3a11ee76/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "lzwcai-mcp-sqlexecutor"
|
||||
version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "httpx" },
|
||||
{ name = "mcp", extra = ["cli"] },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "httpx", specifier = ">=0.28.1" },
|
||||
{ name = "mcp", extras = ["cli"], specifier = ">=1.10.1" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "markdown-it-py"
|
||||
version = "4.0.0"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "mdurl" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/cb0/a2b4aa34f932c/markdown_it_py-4.0.0.tar.gz", hash = "sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/873/27c59b172c501/markdown_it_py-4.0.0-py3-none-any.whl", hash = "sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "mcp"
|
||||
version = "1.10.1"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
{ name = "httpx" },
|
||||
{ name = "httpx-sse" },
|
||||
{ name = "jsonschema" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "pydantic-settings" },
|
||||
{ name = "python-multipart" },
|
||||
{ name = "sse-starlette" },
|
||||
{ name = "starlette" },
|
||||
{ name = "uvicorn", marker = "sys_platform != 'emscripten'" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/aaa/0957d8307feef/mcp-1.10.1.tar.gz", hash = "sha256:aaa0957d8307feeff180da2d9d359f2b801f35c0c67f1882136239055ef034c2" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/4d0/8301aefe906dc/mcp-1.10.1-py3-none-any.whl", hash = "sha256:4d08301aefe906dce0fa482289db55ce1db831e3e67212e65b5e23ad8454b3c5" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
cli = [
|
||||
{ name = "python-dotenv" },
|
||||
{ name = "typer" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "mdurl"
|
||||
version = "0.1.2"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/bb4/13d29f5eea38f/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/840/08a41e51615a4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.12.2"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "annotated-types" },
|
||||
{ name = "pydantic-core" },
|
||||
{ name = "typing-extensions" },
|
||||
{ name = "typing-inspection" },
|
||||
]
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|
||||
{ name = "click" },
|
||||
{ name = "rich" },
|
||||
{ name = "shellingham" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/9ad/824308ded0ad0/typer-0.19.2.tar.gz", hash = "sha256:9ad824308ded0ad06cc716434705f691d4ee0bfd0fb081839d2e426860e7fdca" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/755/e7e19670ffad8/typer-0.19.2-py3-none-any.whl", hash = "sha256:755e7e19670ffad8283db353267cb81ef252f595aa6834a0d1ca9312d9326cb9" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.15.0"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/0ce/a48d173cc12fa/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/f0f/a19c6845758ab/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-inspection"
|
||||
version = "0.4.2"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/ba5/61c48a67c5958/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/4ed/1cacbdc298c22/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "uvicorn"
|
||||
version = "0.37.0"
|
||||
source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" }
|
||||
dependencies = [
|
||||
{ name = "click" },
|
||||
{ name = "h11" },
|
||||
]
|
||||
sdist = { url = "http://devpi.iepai.fun/root/pypi/+f/411/5c8add6d3fd53/uvicorn-0.37.0.tar.gz", hash = "sha256:4115c8add6d3fd536c8ee77f0e14a7fd2ebba939fed9b02583a97f80648f9e13" }
|
||||
wheels = [
|
||||
{ url = "http://devpi.iepai.fun/root/pypi/+f/913/b2b8867234373/uvicorn-0.37.0-py3-none-any.whl", hash = "sha256:913b2b88672343739927ce381ff9e2ad62541f9f8289664fa1d1d3803fa2ce6c" },
|
||||
]
|
||||
9
lzwcai_mcp_sqlexecutor/main.py
Normal file
9
lzwcai_mcp_sqlexecutor/main.py
Normal file
@@ -0,0 +1,9 @@
|
||||
"""
|
||||
Entry point for lzwcai-mcp-sqlexecutor
|
||||
Runs the MCP server for SQL query execution
|
||||
"""
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Import and run the actual MCP server
|
||||
from lzwcai_mcp_sqlexecutor.main import main
|
||||
main()
|
||||
35
lzwcai_mcp_sqlexecutor/pyproject.toml
Normal file
35
lzwcai_mcp_sqlexecutor/pyproject.toml
Normal file
@@ -0,0 +1,35 @@
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "lzwcai-mcp-sqlexecutor"
|
||||
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-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"
|
||||
Reference in New Issue
Block a user