MCP Agentic Rag

@rukshannet

Basic information

About MCP Agentic Rag

MCP Server for Agentic RAG applications

MCP Agentic Rag config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-agentic-rag": {
      "command": "python",
      "args": [
        "server.py"
      ]
    }
  }
}

MCP Agentic Rag tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Frequently asked questions

What are the dependencies of mcp-agentic-rag?

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

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.

What tools does the server provide?

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

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.

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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