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GPT Researcher MCP Server

@assafelovic

代码仓库主页

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

assafelovic

关于 GPT Researcher MCP Server

MCP server for enabling LLM applications to perform deep research via the MCP protocol

#mcp#gpt-researcher#mcp-server

GPT Researcher MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "gptr-mcp": {
      "command": "python",
      "args": [
        "server.py"
      ]
    }
  }
}

GPT Researcher MCP Server 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。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is 🔍 GPT Researcher MCP Server?

🔍 GPT Researcher MCP Server is an MCP server that provides deep, autonomous web research for LLM applications. It integrates with GPT Researcher to explore and validate multiple sources, delivering higher quality and more relevant information than standard web search tools.

How to use 🔍 GPT Researcher MCP Server?

Install dependencies from the repository, configure Claude Desktop with API keys, and run the server using Python, Docker, or the MCP CLI. Use the provided tools such as deep_research, quick_search, and write_report for research tasks.

Key features of 🔍 GPT Researcher MCP Server

  • Provides deep autonomous web research
  • Offers quick search for faster results
  • Generates reports from research data
  • Retrieves sources and full context
  • Optimizes context window usage

Use cases of 🔍 GPT Researcher MCP Server

  • Conducting in-depth investment research on companies
  • Gathering comprehensive information for reports
  • Integrating deep research into Claude Desktop workflows
  • Automating research tasks in n8n or web deployments
  • Obtaining reliable, up-to-date information for LLM reasoning

常见问题

1

What is the difference between `deep_research` and `quick_search`?

`deep_research` performs thorough autonomous analysis and takes about 30 seconds, while `quick_search` returns faster results optimized for speed over depth.

2

What are the prerequisites for running the server?

Python 3.11 or higher, an OpenAI API key, and a Tavily API key. Other search engine APIs are supported as documented.

3

How do I configure Claude Desktop to use this server?

Add a JSON entry to `~/Library/Application Support/Claude/claude_desktop_config.json` with the command to run `server.py` and the required environment variables (`OPENAI_API_KEY`, `TAVILY_API_KEY`).

4

What transport modes does the server support?

STDIO for local clients like Claude Desktop, SSE for Docker and web integrations, and Streamable HTTP for advanced web deployments. The server auto-detects the environment.

5

How are API keys handled in Claude Desktop?

API keys must be explicitly passed in the Claude Desktop configuration file because the server runs as a subprocess and cannot access shell or `.env` variables automatically.

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