Code Scholar

代码学者

上下文感知学术研究MCP服务器。使用混合向量+文本搜索分析代码库并将其链接到相关学术论文。

特性

• 7 MCP工具：搜索路径、获取路径、查找相关论文、获取库状态、分析代码、查找路径、链接路径
• 混合搜索：Vectra矢量数据库（本地，无Docker）+文本关键字匹配
• 代码分析：基于AST的Python、JS、TypeScript概念提取
• 适用性评分：4分量加权评分（概念重叠、文本相关性、新近度、引用）
• 外部发现：在arXiv和Semantic Scholar上搜索新论文

快速开始

# Install Node.js dependencies
npm install

# Install Python dependencies
pip install -r requirements.txt

# Place your unified_library.json in data/
mkdir data
cp /path/to/unified_library.json data/

# Build vector search index (optional, requires OPENAI_API_KEY for embeddings)
node build-vectra-index.js

# Start the MCP server
npm start

MCP配置

添加到您的克劳德代码 .mcp.json:

{
  "mcpServers": {
    "codescholar": {
      "command": "node",
      "args": ["/path/to/CodeScholar/server.js"],
      "env": {
        "OPENAI_API_KEY": "your-key-here"
      }
    }
  }
}

建筑

CodeScholar/
├── server.js                 # MCP server (7 tools)
├── build-vectra-index.js     # One-time index builder
├── config.json               # Server configuration
├── data/                     # Paper library (gitignored)
│   └── unified_library.json
├── vectra-index/             # Vector search index (gitignored)
├── scripts/
│   ├── query_generator.py        # Smart query generation
│   ├── applicability_scorer.py   # Paper relevance scoring
│   ├── generate_embeddings.py    # OpenAI embedding generation
│   ├── analyzers/
│   │   └── concept_extractor.py  # AST-based code analysis
│   ├── sources/
│   │   ├── arxiv_source.py       # arXiv API integration
│   │   └── semantic_scholar_source.py
│   └── tests/                    # Test suites (135 tests)
└── requirements.txt

搜索模式

许可证

麻省理工学院