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Code-Index-MCP (Local-first Code Indexer)

@ViperJuice

代码仓库

基本信息

分类

开发工具

传输方式

stdio

作者

ViperJuice

关于 Code-Index-MCP

Code indexing MCP server to provide context to coding agents.

#code-indexer#local-first#ai-assistant

Code-Index-MCP 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "Code-Index-MCP": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Code-Index-MCP 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Code-Index-MCP?

Code-Index-MCP is a local-first code indexer designed to enhance Claude Code and other LLMs with deep code understanding capabilities. It is built on the Model Context Protocol (MCP) for seamless integration with AI assistants and is intended for developers who need fast, private code analysis.

How to use Code-Index-MCP?

Clone the repository, create a Python virtual environment, install dependencies from requirements.txt, and start the server with uvicorn mcp_server.gateway:app --reload --host 0.0.0.0 --port 8000. Test the API using curl endpoints like /status and /search. Optionally configure via a .env file (e.g., VOYAGE_AI_API_KEY for semantic search). Docker deployment options are also provided.

Key features of Code-Index-MCP

  • Local-first architecture for speed and privacy
  • Plugin-based design for extensibility
  • Multi-language support (Python, C/C++, JavaScript, Dart, HTML/CSS)
  • Real-time file system monitoring for instant index updates
  • AI-powered semantic search with Voyage AI embeddings
  • Rich code intelligence: symbol resolution, type inference, dependency tracking

Use cases of Code-Index-MCP

  • Enhance AI assistants with deep understanding of local codebases
  • Search for code patterns across multiple languages using regex or semantic queries
  • Get symbol definitions and references for navigation and refactoring
  • Index projects in real-time for up-to-date AI-assisted development
  • Deploy as a private, self-hosted code intelligence backend

常见问题

1

What dependencies does Code-Index-MCP require?

Python 3.8+ and several pip packages (listed in `requirements.txt`). For semantic search, a Voyage AI API key is optional. Docker is optional for architecture visualization or containerized deployment.

2

Where does indexed data live?

All indexing happens locally on your machine by default. The local index uses SQLite with FTS5; no data is sent to external servers unless you configure a cloud feature (optional).

3

What languages are supported and how complete is the indexer?

Six languages are fully implemented: Python, C, C++, JavaScript/TypeScript, Dart, and HTML/CSS. The project is 95% complete with a comprehensive testing framework. Rust, Go, Ruby, Swift, Kotlin, Java, and TypeScript are planned.

4

What performance targets does the server meet?

Target SLAs include symbol lookup under 100ms (p95), code search under 500ms (p95), and file indexing of 10,000 files per minute. Memory usage is expected under 2GB for 100,000 files. Core functionality is implemented; benchmark results are pending publication.

5

Which transport and authentication mechanisms are supported?

The server exposes a RESTful API over HTTP (FastAPI) and speaks the MCP protocol. Authentication is not mentioned in the README; the server can be configured with host/port settings in the `.env` file. Security measures include local-first processing, path validation, input sanitization, and secret detection.

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