Agentic RAG with MCP Server - Powerful tools for entity extraction, query refinement, and relevance checking
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# 🚀 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 * …
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