Model Context Protocol Server

# 🚀 Agentic RAG with MCP Server [](https://github.com/ashishpatel26/Agentic-RAG-with-MCP-Server) --- ## ✨ 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. --- ## 🖥️ 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 ```bash # 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`: ```env OPENAI_MODEL_NAME="your-model-name-here" GEMINI_API_KEY="your-model-name-here" ``` --- ## 🚀 How to Use 1. **Start the MCP server:** ```bash python server.py ``` 2. **Run the MCP client:** ```bash python mcp-client.py ``` --- ## 📜 License This project is licensed under the [MIT License](LICENSE). --- ***Thanks for Reading 🙏***