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Model Context Protocol Server

Model Context Protocol Server

by ashishpatel26
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

🚀 Agentic RAG with MCP Server Agentic-RAG-MCPServer - AgenticRag

✨ Overview

Agentic RAG with MCP Server is a powerful project that brings together an MCP (Model Context Protocol) server and client for building Agentic RAG (Retrieval-Augmented Generation) applications.

This setup empowers your RAG system with advanced tools such as:

  • 🕵️‍♂️ Entity Extraction

  • 🔍 Query Refinement

  • ✅ Relevance Checking

The server hosts these intelligent tools, while the client shows how to seamlessly connect and utilize them.

Related MCP server: MCP-RAGNAR

🖥️ Server — server.py

Powered by the FastMCP class from the mcp library, the server exposes these handy tools:

Tool Name

Description

Icon

get_time_with_prefix

Returns the current date & time

⏰

extract_entities_tool

Uses OpenAI to extract entities from a query — enhancing document retrieval relevance

🧠

refine_query_tool

Improves the quality of user queries with OpenAI-powered refinement

✨

check_relevance

Filters out irrelevant content by checking chunk relevance with an LLM

✅

🤝 Client — mcp-client.py

The client demonstrates how to connect and interact with the MCP server:

  • Establish a connection with ClientSession from the mcp library

  • List all available server tools

  • Call any tool with custom arguments

  • Process queries leveraging OpenAI or Gemini and MCP tools in tandem

⚙️ Requirements

  • Python 3.9 or higher

  • openai Python package

  • mcp library

  • python-dotenv for environment variable management

🛠️ Installation Guide

# Step 1: Clone the repository
git clone https://github.com/ashishpatel26/Agentic-RAG-with-MCP-Server.git

# Step 2: Navigate into the project directory
cd Agentic-RAG-with-MCP-Serve

# Step 3: Install dependencies
pip install -r requirements.txt

🔐 Configuration

  1. Create a .env file (use .env.sample as a template)

  2. Set your OpenAI model in .env:

OPENAI_MODEL_NAME="your-model-name-here"
GEMINI_API_KEY="your-model-name-here"

🚀 How to Use

  1. Start the MCP server:

python server.py

  1. Run the MCP client:

python mcp-client.py

📜 License

This project is licensed under the MIT License.

Thanks for Reading 🙏

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