MCP RAG Server

@karaage0703

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基本情報

カテゴリ

メモリとナレッジ

ライセンス

MIT

トランスポート

stdio

作者

karaage0703

MCP RAG Server について

概要はまだありません

MCP RAG Server の設定

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": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

MCP RAG Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

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 Python server that provides Retrieval-Augmented Generation (RAG) functionality compliant with the Model Context Protocol (MCP). It indexes documents in formats such as Markdown, text, PowerPoint, Word, and PDF using the multilingual-e5-large embedding model, and retrieves relevant information via vector search with PostgreSQL and pgvector. It is designed for developers building MCP-based RAG applications.

How to use MCP RAG Server?

Install dependencies with uv sync, set up PostgreSQL 14+ with the pgvector extension, and configure environment variables in .env. Start the MCP server with uv run python -m src.main or use CLI commands like python -m src.cli index to index documents. MCP hosts (e.g., Claude Desktop, Cline, Cursor) can integrate the server via a JSON configuration entry specifying command and args.

Key features of MCP RAG Server

Supports multiple document formats (Markdown, text, PowerPoint, Word, PDF)
Embedding model configurable (default: multilingual-e5-large)
Vector search with PostgreSQL and pgvector
Differential indexing (processes only new or modified files)
Context chunk and full document retrieval
CLI tools for index management and document count

Use cases of MCP RAG Server

Searching document repositories with natural language queries
Building MCP-compatible RAG applications
Indexing and querying slides, PDFs, and text documents
Incrementally updating a document index without reprocessing unchanged files

よくある質問

What are the system requirements?

Python 3.10 or higher and PostgreSQL 14 or higher with the pgvector extension are required.

How do I change the embedding model?

Set the `EMBEDDING_MODEL`, `EMBEDDING_DIM`, and prefix environment variables in `.env`, then clear and reindex using `python -m src.cli clear` and `python -m src.cli index`.

How can I backup and restore the indexed data?

Backup the PostgreSQL database with `pg_dump` and optionally the processed document directory. To restore, set up a new PostgreSQL instance with pgvector, create the database, restore the dump, and copy processed files if needed.

What transport does the server use?

The server uses JSON-RPC over stdio.

How is authentication handled?

Authentication is not mentioned in the README.

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