Agentic RAG With MCP Server
Agentic RAG with MCP Server
Installation
npx agentic-rag-with-mcp-serverAsk AI about Agentic RAG With MCP Server
Powered by Claude Β· Grounded in docs
I know everything about Agentic RAG With MCP Server. Ask me about installation, configuration, usage, or troubleshooting.
0/500
Reviews
Documentation
π Agentic RAG with MCP Server 
β¨ 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:
- π΅οΈββοΈ Entity Extraction
- π Query Refinement
- β Relevance Checking
The server hosts these intelligent tools, while the client shows how to seamlessly connect and utilize them.
π₯οΈ Server β 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 | β° |
extract_entities_tool | Uses OpenAI to extract entities from a query β enhancing document retrieval relevance | π§ |
refine_query_tool | Improves the quality of user queries with OpenAI-powered refinement | β¨ |
check_relevance | Filters out irrelevant content by checking chunk relevance with an LLM | β |
π€ Client β mcp-client.py
The client demonstrates how to connect and interact with the MCP server:
- Establish a connection with
ClientSessionfrom themcplibrary - List all available server tools
- Call any tool with custom arguments
- Process queries leveraging OpenAI or Gemini and MCP tools in tandem
βοΈ Requirements
- Python 3.9 or higher
openaiPython packagemcplibrarypython-dotenvfor environment variable management
π οΈ 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
π Configuration
- Create a
.envfile (use.env.sampleas a template) - Set your OpenAI model in
.env:
OPENAI_MODEL_NAME="your-model-name-here"
GEMINI_API_KEY="your-model-name-here"
π How to Use
- Start the MCP server:
python server.py
- Run the MCP client:
python mcp-client.py
π License
This project is licensed under the MIT License.
Thanks for Reading π
