MCP Server for querying enterprise performance benchmark values (企业绩效标准值查询服务)
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"cpbv-mcp-server","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# CPBV MCP Server 企业绩效标准值查询服务 (Enterprise Performance Benchmark Values MCP Server) 这是一个基于 Model Context Protocol (MCP) 的 Python 服务器,提供企业绩效标准值查询功能。 ## 功能特性 1. **查询支持的行业** - 返回所有支持的行业层级结构 2. **查询绩效标准值** - 根据年份、行业和范围查询企业绩效标准值 ## 安装 ### 前置要求 - Python 3.8 或更高版本 - pip 或 uv 包管理器 ### 从 PyPI 安装 ```bash pip install cpbv-mcp-server ``` ### 从源码安装 ```bash # 克隆仓库 git clone <repository-url> cd cpbv_mcp_server # 安装依赖和包 pip install -e . # 或者使用 uv uv pip install -e . ``` ## 使用方法 ### 作为 MCP 服务器运行 ```bash cpbv-mcp-server ``` 或者使用 Python 模块方式: ```bash python -m cpbv_mcp_server.server ``` ### 在 Claude Desktop 中配置 在 Claude Desktop 的配置文件中添加: ```json { "mcpServers": { "cpbv-mcp-server": { "command": "cpbv-mcp-server", "args": [] } } } ``` ### 使用 MCP Inspector 测试 ```bash # 启动服务器 cpbv-mcp-server # 在另一个终端运行 Inspector npx -y @modelcontextprotocol/inspector ``` ## 工具说明 ### 1. list_industries 查询企业绩效标准值支持的行业。 **返回格式:** ```json { "name": "全国国有企业", "children": [ { "name": "工业", "children": [...] } ] } ``` ### 2. get_performance_benchmarks 查询指定行业的企业绩效标准值。 **参数:** - `year` (string): 年份,例如 "2024" - `industry` (string): 行业名称,例如 "煤炭工业" - `scope` (string): 范围,例如 "全行业"、"大型企业"、"中型企业"、"小型企业" **返回格式:** ```json { "dimensions": [ { "name": "盈利回报指标", "items": [ { "name": "净资产收益率", "unit": "%", "benchmarks": { "excellent": 19.9, "good": 14.6, "average": 11.0, "low": 6.2, "poor": -0.6 } } ] } ] } ``` ### 3. list_indicators 查询绩效标准值支持的指标。 **返回格式:** ```json { "绩效指标分类": [ { "category": "盈利回报指标", "indicators": ["净资产收益率", "营业收入利润率", "总资产报酬率", "盈余现金保障倍数"] }, { "category": "资产运营指标", "indicators": ["总资产周转率", "应收账款周转率", "流动资产周转率", "两金占流动资产比重"] }, ... ] } ``` ### 4. list_available_industries_and_scopes 查询数据库中提供哪些行业和范围的绩效标准值。 **参数:** - `year` (stri…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.