KanishkJagya1/MCP-server

🌐 MCP Server – Multi-LLM Web Search Bridge

A lightweight, extensible backend that leverages Google Gemini and Anthropic Claude to perform intelligent web-assisted queries through DuckDuckGo. The MCP Server extracts meaningful search topics from natural language input and fetches relevant information from the internet.

✨ Features

• Multi-LLM Support: Compatible with Google Gemini and Anthropic Claude
• Intelligent Query Processing: Extracts search topics from natural language
• Web Search Integration: Uses DuckDuckGo for reliable web results
• Multiple Interfaces: Flask API, CLI tool, and Streamlit frontend
• Easy Configuration: Environment-based setup with provider switching

🏗️ Project Structure

MCP-server/
├── mcp_server.py          # Flask API server
├── mcp_integration.py     # Core logic (LLM handling + search)
├── ask_llm.py            # Command-line interface
├── streamlit_app.py      # Interactive web frontend
├── requirements.txt      # Python dependencies
├── .env                  # Environment variables (not committed)
├── .gitignore           # Git ignore rules
└── README.md            # This file

🚀 Quick Start

Prerequisites

• Python 3.8+
• Google Gemini API key or Anthropic Claude API key
• Internet connection for web searches

Installation

1. Clone the repository

   git clone https://github.com/KanishkJagya1/MCP-server.git
   cd MCP-server
   

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Configure environment variables

   Create a .env file in the root directory:

   GEMINI_API_KEY=your_google_gemini_api_key_here
   CLAUDE_API_KEY=your_anthropic_api_key_here
   LLM_PROVIDER=gemini  # Options: "gemini" or "claude"
   PORT=5001
   

🖥️ Usage

Flask API Server

Start the backend server:

python mcp_server.py

Available endpoints:

• GET /health - Health check
• GET / - Server info
• POST /tool_call - Web search endpoint

Example API call:

curl -X POST http://localhost:5001/tool_call \
  -H "Content-Type: application/json" \
  -d '{
    "name": "fetch_web_content",
    "parameters": {
      "query": "latest Mars discoveries"
    }
  }'

Command Line Interface

Ask questions directly from the terminal:

python ask_llm.py "What are the latest developments in AI?"

Streamlit Frontend

Launch the interactive web interface:

streamlit run streamlit_app.py

Then open http://localhost:8501 in your browser.

Example queries:

• "What has NASA discovered on Mars recently?"
• "Tell me about the latest AI breakthroughs"
• "What's happening in renewable energy?"

🤖 Supported LLM Providers

Switch between providers by updating the LLM_PROVIDER in your .env file.

📋 API Response Format

{
  "results": [
    {
      "title": "Example Search Result",
      "url": "https://example.com",
      "description": "Description of the search result..."
    }
  ]
}

🚀 Deployment

Local Development

The server runs on localhost:5001 by default. Configure the port in your .env file.

Production Deployment

Deploy on platforms like:

• Streamlit Cloud (for frontend)
• Render / Railway / Replit (for backend)
• Docker (containerized deployment)

For external access, update the Streamlit app to point to your public Flask URL.

🎯 Use Cases

• Research Assistance: Automated information gathering
• Academic Fact-Checking: Verify claims and sources
• Content Exploration: Discover related topics and trends
• News Analysis: Stay updated with current events
• Smart Search Bots: Build intelligent search applications

🔧 Configuration

Environment Variables

Git Configuration

The project includes a .gitignore file to exclude sensitive files:

__pycache__/
*.pyc
.env
.venv/
*.log
.vscode/
.idea/

If you accidentally committed files before adding .gitignore:

git rm -r --cached .
git add .
git commit -m "Apply .gitignore changes"

🛠️ Development

Testing the API

Test endpoints manually or create automated tests:

# Health check
curl http://localhost:5001/health

# Search query
curl -X POST http://localhost:5001/tool_call \
  -H "Content-Type: application/json" \
  -d '{"name": "fetch_web_content", "parameters": {"query": "test query"}}'

Adding New Features

The modular structure makes it easy to:

• Add new LLM providers in mcp_integration.py
• Extend API endpoints in mcp_server.py
• Enhance the frontend in streamlit_app.py

📝 Roadmap

• Add result summarization using LLMs
• Implement logging and error tracking
• Add caching for repeated queries
• UI enhancements (dark mode, result cards)
• Support for additional search engines
• Batch query processing
• Result export functionality

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the file for details.

👨‍💻 Author

Kanishk Jagya
Thapar Institute of Engineering and Technology
📧 GitHub Profile

🙏 Acknowledgments

• Google Gemini and Anthropic Claude for LLM capabilities
• DuckDuckGo for search functionality
• Streamlit for the web interface framework
• Flask for the API backend

Need help? Open an issue or reach out via GitHub!