🔍 GPT Researcher MCP Server

Name: 🔍 GPT Researcher MCP Server
Author: assafelovic

@assafelovic

Repository Homepage

About 🔍 GPT Researcher MCP Server

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

#mcp #gpt-researcher #mcp-server

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

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Overview

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

FAQ from 🔍 GPT Researcher MCP Server

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.

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.

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).

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.

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.