Gemini-Router

🚀 MCP Server using Gemini - GeminiRouter

🔍 About

This project is a working implementation of the Model Context Protocol (MCP) integrated with Google's Gemini Flash 1.5 API, designed to demonstrate how modular AI services can collaborate through a centralized router.

🧐 What is MCP and How Does It Work Here?

Model Context Protocol (MCP) is an architectural pattern designed to enable modular and context-aware communication between multiple specialized AI agents or services. This project applies MCP principles to build a scalable, intelligent AI system with dedicated microservices, all coordinated through a central router.

Each service (or "context")—whether for chat, web search, weather info, deep reasoning, or retrieval-augmented generation (RAG)—performs a distinct function and communicates via lightweight requests routed intelligently through a Router/Client.

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↺ Workflow Overview

Here's how the system works from input to response:

1. User Input: The user sends a query from the frontend UI.

2. Routing Logic:

   • The Router/Client receives the query and analyzes the intent.
   • Based on keywords, context, or past interactions, it selects the appropriate server (chat, search, RAG, think, etc.) to handle the query.

3. Service Handling:

   • Chat Server: If the query is conversational or casual, it’s routed here for quick responses.
   • Search Server: For queries that need real-time or factual data (e.g., “What’s the capital of Sweden?”), the router invokes Gemini APIs via this module.
   • RAG Server: For complex questions needing document-based or external knowledge synthesis, the RAG module uses a retrieval layer + Gemini for answer generation.
   • Thinking Server: For logical reasoning or multi-step problem solving, the query is handed over here for deep thought processing.
   • Weather (via Search): The Search server also integrates with the OpenWeather API to handle natural language weather queries like "What's the weather in Tokyo?"

4. Response Aggregation:

   • The chosen module processes the request and sends a response back to the Router.
   • The Router can optionally combine responses from multiple modules if the query requires it (e.g., a response that includes weather + a suggestion).

5. Frontend Output: The final response is returned to the user interface and presented in a clean, conversational format.

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🚀 Features

• Modular AI Services: Each function (chat, search, RAG, think) runs independently as a microservice.
• Centralized Context Router: Handles request dispatching, context management, and coordination between services.
• Gemini Flash 1.5: High-performance model for reasoning, generation, and information retrieval.
• OpenWeather Integration: Real-time weather data included in the search context.
• Dockerized Backend: Easily deploy all services in isolation or as a single stack using Docker.

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⚙️ Prerequisites

• Python 3.12+
• Node.js & npm
• Docker & Docker Compose
• Environment variables configured in .env

GEMINI_API_KEY=<your_gemini_api_key>
OPENWEATHER_API_KEY=<your_openweather_api_key>

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🧱 Architecture

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🛠️ How to Run

1. Clone the Repository

git clone https://github.com/AditHash/gemini-mcp-router.git
cd gemini-mcp-router

2. Start the Backend

cd backend
docker-compose up --build

3. Start the Frontend

npm install
npm run dev

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🗂️ Project Structure

backend/
│
├── chat/           # Chat server (basic conversation handling)
├── search/         # Web search & weather functionality
├── rag/            # Retrieval-Augmented Generation logic
├── think/          # Deep reasoning and logic module
├── router/         # Central request dispatcher and router
│
├── requirements.txt
└── .env            # API keys for Gemini and OpenWeather

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📈 Future Plans

• ⚙️ Integrate frontend into Docker-compose setup
• 🎨 Improve UI with modern components and chat UX
• 📊 Add real-time monitoring & logs for each service
• 🤖 Extend MCP with dynamic memory and agent-based reasoning
• ↺ Build support for auto-scaling and horizontal load distribution

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📝 Notes

• Ensure valid API keys in .env for Gemini and OpenWeather services.
• Use docker logs <container_name> to debug any backend issues.