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MCP Agentic Rag

@rukshannet

リポジトリ

基本情報

カテゴリ

推論

トランスポート

stdio

作者

rukshannet

MCP Agentic Rag について

MCP Server for Agentic RAG applications

MCP Agentic Rag 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-agentic-rag": {
      "command": "python",
      "args": [
        "server.py"
      ]
    }
  }
}

MCP Agentic Rag 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-agentic-rag?

mcp-agentic-rag is an MCP server and client implementation for building agentic Retrieval-Augmented Generation (RAG) applications. It exposes tools that enhance RAG performance, such as entity extraction, query refinement, and relevance checking.

How to use mcp-agentic-rag?

Clone the repository, install dependencies (pip install -r requirements.txt), configure the OPENAI_MODEL_NAME environment variable in a .env file, then start the server with python server.py and run the client with python mcp-client.py.

Key features of mcp-agentic-rag

  • Exposes tools for entity extraction, query refinement, and relevance checking.
  • Uses OpenAI models for LLM-based operations.
  • Built with FastMCP from the mcp library.
  • Client demonstrates connection, tool listing, and tool invocation.
  • Supports environment-based configuration via .env file.

Use cases of mcp-agentic-rag

  • Improve document retrieval by extracting key entities from user queries.
  • Enhance query quality before retrieval with LLM-based refinement.
  • Filter irrelevant documents by checking relevance of text chunks to the question.
  • Build a complete agentic RAG pipeline with tool orchestration.

よくある質問

1

What are the dependencies of mcp-agentic-rag?

The server requires Python 3.7+ and the packages `openai`, `mcp`, and `dotenv`.

2

How do I configure the server?

Create a `.env` file based on the provided `.env.sample` and set the `OPENAI_MODEL_NAME` environment variable to the desired OpenAI model.

3

What tools does the server provide?

The server provides four tools: `get_time_with_prefix`, `extract_entities_tool`, `refine_query_tool`, and `check_relevance`.

4

How do I start and interact with the server?

Start the server with `python server.py`. Run the client with `python mcp-client.py` to connect, list tools, and call them with arguments.

5

Does the server require an OpenAI API key?

Yes, the tools that use OpenAI (entity extraction, query refinement, relevance checking) require the `OPENAI_MODEL_NAME` environment variable to be set, which implies an OpenAI API key must be configured in the environment or `.env` file.

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