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mcp-rag-server

@kwanLeeFrmVi

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mcp-rag-server について

mcp-rag-server is a Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG) capabilities. It empowers Large Language Models (LLMs) to answer questions based on your document content by indexing and retrieving relevant information efficiently.

#rag#mcp-server

基本情報

カテゴリ

メモリとナレッジ

ライセンス

MIT

ランタイム

node

トランスポート

stdio

公開者

kwanLeeFrmVi

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-rag-server-kwanleefrmvi": {
      "command": "npx",
      "args": [
        "mcp-rag-server"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is mcp-rag-server?

mcp-rag-server is a Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG). It indexes local documents and serves relevant context to Large Language Models, integrating with MCP-compatible clients.

How to use mcp-rag-server?

Install globally (npm install -g mcp-rag-server) or run via npx. Set environment variables (BASE_LLM_API, EMBEDDING_MODEL, VECTOR_STORE_PATH, CHUNK_SIZE) then start the server. Use MCP tools like embedding_documents and query_documents to index and retrieve content. An example MCP client configuration is provided in the README.

Key features of mcp-rag-server

  • Index documents in .txt, .md, .json, .jsonl, and .csv formats
  • Customizable chunk size for text splitting
  • Local vector store powered by SQLite via LangChain’s LibSQLVectorStore
  • Supports multiple embedding providers (OpenAI, Ollama, Granite, Nomic)
  • Exposes MCP tools and resources over stdio for seamless integration

Use cases of mcp-rag-server

  • Build a RAG pipeline for local document corpora
  • Provide LLMs with context from private or offline files
  • Index and query large collections of markdown or code documentation
  • Enable question-answering systems over custom datasets

FAQ from mcp-rag-server

Which embedding providers are supported?

The server supports OpenAI, Ollama, Granite, and Nomic. Ollama with the nomic-embed-text model is recommended for best performance.

What file formats can be indexed?

It supports .txt, .md, .json, .jsonl, and .csv files.

How does the server store vectors?

Vectors are stored locally in a SQLite database via LangChain’s LibSQLVectorStore. The path is configured with the VECTOR_STORE_PATH environment variable.

What configuration variables are required?

Key variables are BASE_LLM_API, EMBEDDING_MODEL, VECTOR_STORE_PATH, and CHUNK_SIZE. An optional LLM_API_KEY can be set for providers that require it.

How do I run the server?

Install globally (npm install -g mcp-rag-server) and run mcp-rag-server, or use npx mcp-rag-server. Ensure all required environment variables are set before starting.

コメント

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