🔍 GPT Researcher MCP Server

Why GPT Researcher 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.

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:

• ✨ Higher quality information
• 📊 Optimized context usage
• 🔎 Comprehensive results
• 🧠 Better reasoning for LLMs

💻 Claude Desktop Demo

https://github.com/user-attachments/assets/ef97eea5-a409-42b9-8f6d-b82ab16c52a8

🚀 Quick Start with Claude Desktop

Want to use this with Claude Desktop right away? Here's the fastest path:

1. Install dependencies:

   git clone https://github.com/assafelovic/gptr-mcp.git
   pip install -r requirements.txt
   

2. Set up your Claude Desktop config at ~/Library/Application Support/Claude/claude_desktop_config.json:

   {
     "mcpServers": {
       "gptr-mcp": {
         "command": "python",
         "args": ["/absolute/path/to/gpt-researcher/gptr-mcp/server.py"],
         "env": {
           "OPENAI_API_KEY": "your-openai-key-here",
           "TAVILY_API_KEY": "your-tavily-key-here"
         }
       }
     }
   }
   

3. Restart Claude Desktop and start researching! 🎉

For detailed setup instructions, see the full Claude Desktop Integration section below.

Resources

• research_resource: Get web resources related to a given task via research.

Primary Tools

• 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. Supports any GPTR supported web retriever such as Tavily, Bing, Google, etc... Learn more here
• 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

Prompts

• research_query: Create a research query prompt

Prerequisites

Before running the MCP server, make sure you have:

1. Python 3.11 or higher installed
   • Important: GPT Researcher >=0.12.16 requires Python 3.11+
2. API keys for the services you plan to use:
   • OpenAI API key
   • Tavily API key

You can also connect any other web search engines or MCP using GPTR supported retrievers. Check out the docs here

⚙️ Installation

1. Clone the GPT Researcher repository:

git clone https://github.com/assafelovic/gpt-researcher.git
cd gpt-researcher

2. Install the gptr-mcp dependencies:

cd gptr-mcp
pip install -r requirements.txt

3. Set up your environment variables:
   • Copy the .env.example file to create a new file named .env:

   cp .env.example .env
   

   • Edit the .env file and add your API keys and config

... View full README on GitHub