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GPT Researcher MCP Server

@assafelovic

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基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

assafelovic

GPT Researcher MCP Server について

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

#mcp#gpt-researcher#mcp-server

GPT Researcher MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

GPT Researcher MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

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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