conda activate MCP
pip install -r requirements.txt
# 或
pip install -e .

SCHOLAR_PROXY  >  HTTP_PROXY / HTTPS_PROXY  >  默认 http://localhost:7890

{
  "scholar-search": {
    "command": "C:/Users/mulim/.conda/envs/MCP/python.exe",
    "args": ["C:/Users/mulim/Desktop/Project/scholar_search/server.py"],
    "env": {
      "SCHOLAR_PROXY": "http://localhost:7890",
      "SCHOLAR_CHART_PORT": "8765"
    }
  }
}

search_papers → analyze_relevance → generate_relevance_chart → 浏览器打开 http://localhost:8765

http://localhost:8765/

# 运行测试（默认跳过网络 mock 测试，~7s）
pytest

# 包含网络 mock 测试（~40s）
pytest tests/ --ignore=

# 带 coverage.xml 输出
pytest --cov=scholar_search --cov=server --cov-report=term-missing --cov-report=xml

# Lint 检查 (PEP 8, max-line=127)
flake8 --max-line-length=127 .

# 启动调试
python server.py

scholar_search/
├── server.py              # MCP Server 入口 (FastMCP, 4 个 Tool)
├── scholar_search/        # 核心包
│   ├── config.py          # 代理 / 超时 / 重试 / 端口配置
│   ├── search.py          # requests + bs4 直连解析，外部源完整摘要（arxiv API / meta 标签）
│   ├── analysis.py        # TF-IDF + 余弦相似度 + 方向聚类
│   └── viz.py             # Matplotlib 多图表 + HTTP 服务端
├── tests/                 # pytest (137 tests, 100% 覆盖)
│   ├── test_config.py
│   ├── test_search.py
│   ├── test_analysis.py
│   ├── test_viz.py
│   └── test_server.py
├── requirements.txt
├── pyproject.toml
├── CLAUDE.md
└── .gitignore