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DuckDuckGo Search with MCP Agent

@alihassanml

代码仓库

基本信息

分类

搜索

许可证

MIT

传输方式

stdio

作者

alihassanml

关于 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).

#duckduckgo#mcp-server

DuckDuckGo Search with MCP Agent 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "Duckduckgo-with-MCP": {
      "command": "uvx",
      "args": [
        "-y",
        "duckduckgo-mcp-server"
      ]
    }
  }
}

DuckDuckGo Search with MCP Agent 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is DuckDuckGo Search with MCP Agent?

DuckDuckGo Search with MCP Agent is a project that demonstrates how to use the DuckDuckGo MCP Server with a LangChain Groq LLM agent to perform intelligent search tasks via MCP (Micro Component Protocol). It is intended for developers building search-driven applications with agentic reasoning.

How to use DuckDuckGo Search with MCP Agent?

Clone the repository, install dependencies with pip install -r requirements.txt, set your GROQ_API_KEY in a .env file, install the MCP server via uvx -y duckduckgo-mcp-server, and run python main.py. The script starts an MCP client, connects to the DuckDuckGo MCP server, and uses the Groq LLM to perform a smart search.

Key features of DuckDuckGo Search with MCP Agent

  • DuckDuckGo MCP Server integration
  • Groq LLM (deepseek-r1-distill-llama-70b) for reasoning
  • Async Python execution
  • Simple and modular code structure

Use cases of DuckDuckGo Search with MCP Agent

  • Performing intelligent web searches with an LLM agent
  • Demonstrating MCP client-server integration with LangChain
  • Building modular search pipelines that reason over results

常见问题

1

What dependencies are required?

The project requires libraries such as `langchain_groq` and `python-dotenv`, listed in `requirements.txt`. You also need `uvx` installed to run the DuckDuckGo MCP server.

2

What LLM model does the agent use?

The agent uses Groq's `deepseek-r1-distill-llama-70b` model for reasoning.

3

How do I get started quickly?

Set up your `.env` file with a valid `GROQ_API_KEY`, install all Python dependencies, and run `python main.py`. The script includes an example that searches for "the best restaurant in San Francisco."

4

What transport protocol does the server use?

The server uses MCP (Micro Component Protocol) over stdio, launched via the `uvx` command.

5

Is there a license for this project?

Yes, this project is licensed under the MIT License.

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