From 9ea81fe3e71f6aa87ba41d4d64d24020ce779f01 Mon Sep 17 00:00:00 2001
From: yuanzhipeng <2501363769@qq.com>
Date: Thu, 8 Jan 2026 09:51:04 +0800
Subject: [PATCH] =?UTF-8?q?chore(analyzeWorkOrder):=20=E9=87=8D=E5=91=BD?=
 =?UTF-8?q?=E5=90=8D=E5=8C=85=E5=90=8D=E4=B8=BAanalyzeWorkOrder=E5=B9=B6?=
 =?UTF-8?q?=E6=9B=B4=E6=96=B0=E9=85=8D=E7=BD=AE?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 将包名从 lzwcai-mcpskills-analyzeOrder 重命名为 lzwcai-mcpskills-analyzeWorkOrder
- 更新 pyproject.toml 中的项目名称、脚本入口点和包配置
- 更新包路径相关的配置项
- 日志文件中显示了MCP服务器运行的相关信息
---
 .../pyproject.toml                            |  8 +-
 .../logs/lzwcai_mcp_sqlexecutor.log           | 98 +++++++++++++++++++
 .../logs/lzwcai_mcp_sqlexecutor_daily.log     | 98 +++++++++++++++++++
 .../logs/mcp_services.log                     | 44 +++++++++
 .../lzwcai_mcpskills_mfg_data_agent/main.py   |  2 +-
 .../pyproject.toml                            | 12 +--
 6 files changed, 251 insertions(+), 11 deletions(-)

diff --git a/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml b/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml
index 0f9a2b3..06bad03 100644
--- a/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml
+++ b/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml
@@ -3,7 +3,7 @@ requires = ["hatchling"]
 build-backend = "hatchling.build"
 
 [project]
-name = "lzwcai-mcpskills-analyzeOrder"
+name = "lzwcai-mcpskills-analyzeWorkOrder"
 version = "0.1.12"
 description = "MCP server for executing business SQL queries with dynamic tool generation"
 readme = "README.md"
@@ -26,10 +26,10 @@ dependencies = [
 ]
 
 [project.scripts]
-lzwcai-mcpskills-analyzeOrder = "lzwcai_mcpskills_analyzeOrder.main:main"
+lzwcai-mcpskills-analyzeWorkOrder = "lzwcai_mcpskills_analyzeWorkOrder.main:main"
 
 [tool.hatch.build.targets.wheel]
-packages = ["lzwcai_mcpskills_analyzeOrder"]
+packages = ["lzwcai_mcpskills_analyzeWorkOrder"]
 
 [tool.hatch.build.targets.wheel.force-include]
-"lzwcai_mcpskills_analyzeOrder/businessQueries.json" = "lzwcai_mcpskills_analyzeOrder/businessQueries.json"
+"lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json" = "lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json"
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log
index 7327bf8..677d99f 100644
--- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log
@@ -231,3 +231,101 @@
 2026-01-08 00:30:44 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
 2026-01-08 00:30:44 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
 2026-01-08 00:30:44 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:38:55 - 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_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs
+2026-01-08 00:38:55 - 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
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-08 00:38:57 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-08 00:38:59 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-08 00:38:59 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis
+2026-01-08 00:38:59 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema
+2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "OrderDelayWarningAnalysis",
+  "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级",
+  "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-08 00:38:59 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
+2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-08 00:38:59 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:52:34 - 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_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs
+2026-01-08 00:52:34 - 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
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-08 00:52:36 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-08 00:52:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-08 00:52:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes
+2026-01-08 00:52:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema
+2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "WorkOrderProgressAndAnomalyNodes",
+  "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点",
+  "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-08 00:52:37 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
+2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-08 00:52:37 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:53:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-08 00:53:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-08 00:53:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema
+2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "OnePageDecisionBrief",
+  "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报",
+  "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-08 00:53:21 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
+2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-08 00:53:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:56:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-08 00:56:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-08 00:56:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema
+2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "OnePageDecisionBrief",
+  "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报",
+  "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-08 00:56:21 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
+2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-08 00:56:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log
index 1346525..ba82ae7 100644
--- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log
@@ -107,3 +107,101 @@
 2026-01-08 00:16:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
 2026-01-08 00:16:13 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
 2026-01-08 00:16:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:38:55 - 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_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs
+2026-01-08 00:38:55 - 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
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-08 00:38:57 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-08 00:38:59 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-08 00:38:59 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis
+2026-01-08 00:38:59 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema
+2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "OrderDelayWarningAnalysis",
+  "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级",
+  "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-08 00:38:59 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
+2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-08 00:38:59 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:52:34 - 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_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs
+2026-01-08 00:52:34 - 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
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-08 00:52:36 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-08 00:52:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-08 00:52:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes
+2026-01-08 00:52:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema
+2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "WorkOrderProgressAndAnomalyNodes",
+  "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点",
+  "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-08 00:52:37 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
+2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-08 00:52:37 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:53:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-08 00:53:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-08 00:53:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema
+2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "OnePageDecisionBrief",
+  "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报",
+  "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-08 00:53:21 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
+2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-08 00:53:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:56:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-08 00:56:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-08 00:56:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema
+2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "OnePageDecisionBrief",
+  "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报",
+  "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-08 00:56:21 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 "
+2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-08 00:56:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log
index 7cca1e2..5beacee 100644
--- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log
@@ -69,3 +69,47 @@
 2026-01-08 00:30:44 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning
 2026-01-08 00:30:44 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
 2026-01-08 00:30:44 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-08 00:38:55 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-08 00:38:57 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-08 00:38:59 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis
+2026-01-08 00:38:59 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:38:59 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-08 00:52:34 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-08 00:52:36 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-08 00:52:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes
+2026-01-08 00:52:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:52:37 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:53:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-08 00:53:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:53:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-08 00:56:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-08 00:56:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-08 00:56:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/main.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/main.py
index 07f4694..71cb13f 100644
--- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/main.py
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/main.py
@@ -94,7 +94,7 @@ def generate_tool_schema_from_query(query: dict) -> types.Tool:
 
 
 # 创建 MCP 服务器实例
-server = Server("lzwcai-mcpskills-analyzeOrder")
+server = Server("lzwcai-mcpskills-mfg-data-agent")
 
 # 缓存查询配置，避免重复加载
 _queries_cache = None
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/pyproject.toml b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/pyproject.toml
index 3532f74..d6139e9 100644
--- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/pyproject.toml
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/pyproject.toml
@@ -3,16 +3,16 @@ requires = ["hatchling"]
 build-backend = "hatchling.build"
 
 [project]
-name = "lzwcai-mcpskills-analyzeWorkOrder"
+name = "lzwcai-mcpskills-mfg-data-agent"
 version = "0.1.1"
-description = "MCP server for executing business SQL queries with dynamic tool generation"
+description = "制造业数据智能体 - MCP server for manufacturing data intelligence 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"]
+keywords = ["mcp", "sql", "manufacturing", "data", "agent", "智能体"]
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -26,10 +26,10 @@ dependencies = [
 ]
 
 [project.scripts]
-lzwcai-mcpskills-analyzeWorkOrder = "lzwcai_mcpskills_analyzeWorkOrder.main:main"
+lzwcai-mcpskills-mfg-data-agent = "lzwcai_mcpskills_mfg_data_agent.main:main"
 
 [tool.hatch.build.targets.wheel]
-packages = ["lzwcai_mcpskills_analyzeWorkOrder"]
+packages = ["lzwcai_mcpskills_mfg_data_agent"]
 
 [tool.hatch.build.targets.wheel.force-include]
-"lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json" = "lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json"
+"lzwcai_mcpskills_mfg_data_agent/businessQueries.json" = "lzwcai_mcpskills_mfg_data_agent/businessQueries.json"