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Procplan MCP Server

MCP Server

一个轻量级的MCP服务器,提供读取项目数据的代理功能,支持本地或远程Projects REST服务。

工具数

1

提示词数

0

GitHub Stars

0

资源数

0
PythonFastMCP云端部署

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

作者 / 组织

AI-CodeGen

提供方

AI-CodeGen

最后核验

2026/5/17 20:19

运行时

Python

快速接入

先看主来源和安装命令,再打开仓库或文档;下面只保留这个条目的关键接入事实。

命令预览

uv run python server.py

详细介绍

procplan-mcp-server

Minimal MCP server exposing a single tool read_projects that proxies to a local (or remote) Projects REST service.

Upgraded to FastMCP v2 – using the modern from fastmcp import FastMCP import and @mcp.tool decorator style. Earlier examples that used from mcp.server.fastmcp import FastMCP or @server.tool() are now deprecated here.

Tool: read_projects

Inputs:

  • project_id (optional, string): If supplied fetches /projects/, otherwise /projects.

Output:

  • JSON object: If upstream returned a list it is wrapped as { "projects": [...], "count": N }, otherwise the JSON is passed through.

Environment Variables

  • PROJECTS_SERVICE_URL (default: http://localhost:5001)

server.py calls load_dotenv() at import time, so a local .env file in the project root will be read automatically.

Ways to set PROJECTS_SERVICE_URL

  1. Inline per command (fastest):
	PROJECTS_SERVICE_URL="http://localhost:5005" uv run python server.py
  1. Export in current shell session:
	export PROJECTS_SERVICE_URL="http://localhost:5005"
	uv run python server.py
  1. .env file (auto-loaded):
	PROJECTS_SERVICE_URL=http://localhost:5005
  1. Wrapper script (run.sh):
	#!/usr/bin/env bash
	export PROJECTS_SERVICE_URL="http://projects:5005"
	uv run python server.py
  1. Docker / Compose:
	services:
	  mcp-server:
		 image: your-image
		 environment:
			PROJECTS_SERVICE_URL: http://projects:5005
  1. systemd service (Linux):
	[Service]
	Environment=PROJECTS_SERVICE_URL=http://projects:5005
	ExecStart=/usr/bin/uv run python /opt/procplan-mcp-server/server.py
  1. Client integration (MCP host app): configure env before spawning the process.

If unset, the server falls back to http://localhost:5001.

Expected Upstream API

GET /projects            -> 200 JSON list
GET /projects/       -> 200 JSON object

Run the Server (stdio)

uv run python server.py

Install / Sync Dependencies

After cloning or changing dependencies:

uv sync           # installs fastmcp, httpx, dotenv (stdlib usage only) per pyproject.toml

If you previously used the legacy SDK:

uv remove mcp[cli]
uv add fastmcp>=2.0.0

List Tools (manual test)

echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | uv run python server.py

Call Tool (list projects)

echo '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"read_projects","arguments":{}}}' | uv run python server.py

Call Tool (single project)

echo '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"read_projects","arguments":{"project_id":"123"}}}' | uv run python server.py

Add Dependency / Lock Update

uv add httpx

Notes

  • Uses mcp[cli] fast server helper (FastMCP).
  • Network timeouts set to 10s.
  • Non-2xx responses surface as tool errors to the client.

FastMCP v2 Differences

  • Import path: from fastmcp import FastMCP
  • Decorator style: @mcp.tool vs older @server.tool()
  • Same mcp.run() entry point for stdio execution
  • Designed for richer ecosystem (composition, auth, proxies). This project only uses the minimal tool exposure.

Upgrade Checklist (from older implementation)

  1. Replace legacy import with from fastmcp import FastMCP
  2. Rename instance variable (e.g. server -> mcp for clarity)
  3. Change decorators to @mcp.tool
  4. Update dependency in pyproject.toml
  5. Run uv sync
  6. (Optional) Add new tools / resources using FastMCP 2.x patterns

Planned Enhancements (Optional)

  • Add a search_projects tool with query filtering
  • Introduce retries & exponential backoff for transient upstream failures
  • Add JSON schema validation of upstream responses
  • Provide OpenAPI-like resource exposure using FastMCP composition patterns

目录标签

目录标签

PythonFastMCP云端部署REST代理本地部署项目数据管理轻量级服务器

接入字段

传输方式(transport,传输协议)

stdio

鉴权方式(authType,认证方式)

session

运行时(runtime,运行环境)

Python

工具数量(toolCount,工具数)

1

资源数量(resourceCount,资源数)

0

提示词数量(promptCount,提示词数)

0

权限和风险

stdiosession部署方式未说明

接入前请确认传输方式、认证方式和部署位置,并根据实际工具能力限制访问范围。

安装前确认

不要直接授予不必要的文件、网络或账号权限;先核对安装命令和配置内容。

来源信息

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