mcp-personal

mcp-personal

A personal MCP server built with Python FastMCP, offering tools for paper retrieval, dataset analysis, project scaffolding, and directory utilities, with stdio and HTTP transport plus optional bearer token authentication.

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README

mcp-personal · 个人 MCP 服务器

用 Python(FastMCP)构建的个人 MCP 服务器,可接入 Cursor / VS Code / DeepSeek Harness 等任意 MCP 客户端。

完整规划见 PLAN.md · 展示素材见 docs/展示指南.md · 当前进度:四大场景 + HTTP + 鉴权 + pdf_extract ✅ · 21 个测试通过 · 环境变量见 .env.example

快速开始

# 1. 创建虚拟环境并安装(需 Python 3.11+ 或 uv)
uv venv
uv pip install -e ".[dev]"

# 2. 跑测试
uv run pytest tests -q

# 3. 启动服务器(stdio,供客户端接入)
uv run python -m mcp_personal

# 4. 冒烟测试:真实 stdio 子进程链路
uv run python scripts/smoke_stdio.py

# 5. 查看服务器注册的能力清单(Tools/Prompts/Resources)
uv run python scripts/verify_capabilities.py

# 6. 调试:官方 MCP Inspector
uv run mcp dev src/mcp_personal/server.py

接入客户端

配置已预置(portable 写法:uv run --project . 自动定位环境,仓库可任意移动):

客户端 配置文件
Cursor .cursor/mcp.json(设置 → MCP → 添加本地服务器)
VS Code .vscode/mcp.json(需安装 "MCP" 扩展 / Copilot Chat)
其他 标准 stdio 配置:command + args 同上

接入后先让 AI 调用 ping 工具验证链路。

HTTP 远程接入(可选)

# 终端 1:启动 HTTP 服务(默认 127.0.0.1:8000,--host 0.0.0.0 可局域网访问)
uv run python scripts/run_http.py --port 8000

# 终端 2:用官方客户端验证链路
uv run python scripts/client_http_check.py --url http://127.0.0.1:8000/mcp

其他远程客户端把 MCP 服务器地址配成 http://<host>:<port>/mcp 即可。

HTTP 鉴权(可选)

设置环境变量后启动,所有请求需携带 Authorization: Bearer <token>

# 终端 1:带鉴权启动
MCP_API_TOKEN=my-secret uv run python scripts/run_http.py --port 8000

# 终端 2:带 token 验证
uv run python scripts/client_http_check.py --url http://127.0.0.1:8000/mcp --token my-secret

说明:基于 FastMCP 官方 StaticTokenVerifier(API key 风格),token 明文存储,仅适合个人/开发用途;生产应改用 OAuth/JWT。

目录结构

├── PLAN.md            # 项目规划书
├── pyproject.toml     # 依赖与打包
├── src/mcp_personal/  # 服务器代码
│   ├── server.py      # FastMCP 入口(注册全部原语)
│   ├── tools/         # Tool:paper(检索)/dataset(摸底)/scaffold(课设)/devtools(目录树)
│   ├── prompts.py     # Prompt:literature_review 文献综述框架
│   └── resources.py   # Resource:papers://list 本地论文库
├── papers/            # 本地论文库(放 PDF)
├── datasets/          # 竞赛数据集(dataset_info 工具使用)
├── tests/             # pytest 测试(13 个)
├── .github/workflows/ # CI:push/PR 自动跑测试
├── scripts/           # smoke_stdio.py / verify_capabilities.py
├── .cursor/ .vscode/  # 客户端接入配置
└── README.md

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