What is Mcp Rag Search Server

The mcp-rag-search-server is a custom MCP server that integrates Retrieval-Augmented Generation (RAG) capabilities with multiple search providers, specifically Google’s Gemini 2.0 and Linkup.

Use cases

Use cases include academic research where local documents are queried, web applications needing advanced search functionalities, and AI-driven content generation platforms.

How to use

To use the mcp-rag-search-server, clone the repository, install the required dependencies, set up the necessary environment variables including API keys, add local documents to the data directory, and start the server using the command ‘python server.py’.

Key features

Key features include RAG workflow using local documents, advanced AI-powered search through Google’s Gemini 2.0, traditional web search via Linkup, and integration with FastMCP for efficient server implementation.

Where to use

The mcp-rag-search-server can be utilized in various fields such as information retrieval, AI research, content generation, and any application requiring enhanced search capabilities.

MCP Server with RAG and Multi-Search

A custom MCP (Model Calling Protocol) server that provides RAG (Retrieval-Augmented Generation) capabilities using LlamaIndex and multiple web search options via Google’s Gemini 2.0 API and Linkup.

Features

• RAG workflow using local documents
• Multiple web search capabilities:
  • Google’s Gemini 2.0 for advanced AI-powered search
  • Linkup for traditional web search
• Built with FastMCP

Setup

Prerequisites

• Python 3.8 or higher
• Ollama installed locally with DeepSeek models (or modify to use your preferred model)
• Gemini API key (get one at https://ai.google.dev/)
• Linkup API key (optional)

Installation

1. Clone this repository:

   git clone <repository-url>
   cd own-mcp-server
   

2. Install required dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables (create a .env file):

   # Required API keys
   GEMINI_API_KEY=your_gemini_api_key_here
   LINKUP_API_KEY=your_linkup_api_key_here
   
   # Optional configurations
   OLLAMA_HOST=http://localhost:11434
   

4. Add documents to the data directory (will be created automatically if it doesn’t exist)

Running the Server

Start the server with:

python server.py

Usage

The server provides the following tools:

1. web_search: Uses the best available search method (Gemini 2.0 preferred, fallback to Linkup)
2. gemini_search: Search using Google’s Gemini 2.0 AI
3. linkup_search: Search using Linkup
4. rag: Query your local documents using RAG

Required Libraries

This project uses:

• llama-index - Core RAG functionality
• ollama - Local LLM integration
• Google Generative AI SDK - Gemini 2.0 integration
• Linkup SDK - Web search capabilities
• FastMCP - MCP server implementation
• Python-dotenv - Environment management
• nest-asyncio - Async support

Troubleshooting

If you encounter issues:

1. Make sure Ollama is properly installed and running
2. Pull the DeepSeek model: ollama pull deepseek-r1:1.5b
3. If you encounter Python 3.13 compatibility issues, consider downgrading to Python 3.11 or 3.10
4. Verify your API keys are correct and have the necessary permissions
5. For Gemini 2.0 issues, make sure your API key has access to the latest models