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R2R MCP Server

@eagurin

代码仓库

基本信息

分类

其他

许可证

MIT license

传输方式

stdio

作者

eagurin

关于 R2R MCP Server

R2R MCP Server provides Model Context Protocol (MCP) with R2R (Retrieval-to-Response) to improve interaction with Claude and other MCP-compatible models, providing access to knowledge bases.

#r2r#mcp#model-context-protocol

R2R MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "r2r-mcp": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

R2R MCP Server 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 🚀 R2R MCP Server?

R2R MCP Server provides a Model Context Protocol (MCP) integration with the R2R (Retrieval-to-Response) system, enabling Claude and other MCP-compatible models to access knowledge bases through tools like search, RAG, web search, and agent research.

How to use 🚀 R2R MCP Server?

Install Python 3.12+, uv, clone the repository, create a virtual environment, install dependencies via make install or uv pip install ., then run mcp install app/server.py -v R2R_API_URL=... or -v R2R_API_KEY=... to register as an MCP plug-in. The server exposes tools such as search, rag, web_search, document_search, list_documents, and agent_research. Use make run to start locally.

Key features of 🚀 R2R MCP Server

  • MCP server for Claude and other MCP-compatible models
  • Asynchronous R2R client for high-performance knowledge base access
  • Search and RAG tools with vector, full‑text, and hybrid search
  • Structured logging via Loguru
  • Lightweight architecture with minimal dependencies
  • Web search and agent research for complex queries

Use cases of 🚀 R2R MCP Server

  • Search documents and retrieve specific text fragments
  • Answer questions with citations from your knowledge base
  • Conduct multi‑source research by combining different information sources
  • Fetch up‑to‑date information from the web when needed

常见问题

1

What tools does the server expose?

It provides `search` (vector/hybrid), `rag` (answer with citations), `web_search`, `document_search`, `list_documents`, and `agent_research` for complex LLM‑driven research.

2

What are the runtime dependencies?

Python ≥3.12, the `uv` package manager, and three core libraries: `mcp`, `r2r`, and `loguru`. Development and testing require additional packages like pytest, black, mypy, etc.

3

How do I configure the R2R API connection?

Set the `R2R_API_URL` (e.g., `https://api.sciphi.ai`) or `R2R_API_KEY` environment variable when installing the server with `mcp install app/server.py -v`.

4

Can the server work with models other than Claude?

Yes—it is compatible with any LLM that supports the Model Context Protocol, including custom MCP clients.

5

Are there performance benchmarks?

Yes, the project includes dedicated performance tests in `test_r2r_mcp_performance.py` that measure formatting speed and tool processing throughput.

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