Duckduckgo-with-MCP

# DuckDuckGo Search with MCP Agent This project demonstrates how to use **DuckDuckGo MCP Server** with a **LangChain Groq LLM** agent to perform intelligent search tasks via MCP (Micro Component Protocol). --- ## Features - **MCP Server Integration** (DuckDuckGo search) - **Groq LLM** (`deepseek-r1-distill-llama-70b`) for reasoning - **Async Python execution** - **Simple and modular** --- ## Installation 1. **Clone the repository**: ```bash git clone https://github.com/alihassanml/Duckduckgo-with-MCP.git cd Duckduckgo-with-MCP ``` 2. **Install dependencies**: ```bash pip install -r requirements.txt ``` (Include libraries like `langchain_groq`, `python-dotenv`, etc. in your `requirements.txt`.) 3. **Set up your `.env` file**: ```env GROQ_API_KEY=your_groq_api_key_here ``` 4. **Install the MCP Server**: ```bash uvx -y duckduckgo-mcp-server ``` *(Make sure `uvx` is installed. If not, install it.)* --- ## Usage Run the main script: ```bash python main.py ``` This will: - Start the MCP client - Connect to the `duckduckgo-mcp-server` - Use the Groq LLM to perform a smart search - Print the result --- ## Example Code ```python import asyncio import os from dotenv import load_dotenv from langchain_groq import ChatGroq from mcp_use import MCPAgent, MCPClient async def main(): load_dotenv() config = { "mcpServers": { "ddg-search": { "command": "uvx", "args": ["-y", "duckduckgo-mcp-server"] } } } client = MCPClient.from_dict(config) llm = ChatGroq(model="deepseek-r1-distill-llama-70b") agent = MCPAgent(llm=llm, client=client, max_steps=30) result = await agent.run("Find the best restaurant in San Francisco") print(f"\nResult: {result}") if __name__ == "__main__": asyncio.run(main()) ``` --- ## Resources - [DuckDuckGo MCP Server](https://www.npmjs.com/package/duckduckgo-mcp-server) - [LangChain Groq Documentation](https://python.langchain.com/docs/integrations/llms/groq) - [Micro Component Protocol (MCP)](https://github.com/openbionics/mcp) --- ## License This project is licensed under the MIT License.