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Gptr MCP

assafelovic/gptr-mcp

While LLM apps can access web search tools with MCP, GPT Researcher MCP delivers deep research results. Standard search tools return raw results requiring manual filtering, often containing irrelevant sources and wasting context window space.

View on GitHub94

Install

docker run -d \

Client configuration

{
  "mcpServers": {
    "gptr-mcp": {
      "command": "python",
      "args": [
        "/absolute/path/to/gpt-researcher/gptr-mcp/server.py"
      ],
      "env": {
        "OPENAI_API_KEY": "<OPENAI_API_KEY>",
        "TAVILY_API_KEY": "<TAVILY_API_KEY>"
      }
    }
  }
}

Environment variables

OPENAI_API_KEYTAVILY_API_KEYMCP_TRANSPORT
GitHub stars
94
Author
@assafelovic
Category
Research & Data, AI Chatbot
License
MIT
Updated
Oct 6, 2026

About Gptr MCP

GPT Researcher autonomously explores and validates numerous sources, focusing only on relevant, trusted and up-to-date information. Though slightly slower than standard search (~30 seconds wait), it delivers:

Tools (5)

  • deep_research

    Performs deep web research on a topic, finding the most reliable and relevant information

  • quick_search

    Performs a fast web search optimized for speed over quality, returning search results with snippets.

  • write_report

    Generate a report based on research results

  • get_research_sources

    Get the sources used in the research

  • get_research_context

    Get the full context of the research

Details on this page are taken from the project's README. Open README

Supported clients

Clients mentioned in this server's README:

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