🔍 GPT Researcher MCP Server

Note: This content has been moved to a dedicated repository: https://github.com/assafelovic/gptr-mcp

Overview

The GPT Researcher MCP Server enables AI assistants like Claude to conduct comprehensive web research and generate detailed reports via the Machine Conversation Protocol (MCP).

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

Features

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 reliable and relevant information
quick_search: Performs a fast web search optimized for speed over quality
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

Installation

For detailed installation and usage instructions, please visit the official repository.

Quick start:

Clone the new repository:

bash

git clone https://github.com/assafelovic/gptr-mcp.git
cd gptr-mcp

Install dependencies:
bash
```
pip install -r requirements.txt
```

Create a .env file with your API keys:

OPENAI_API_KEY=your_openai_api_key
TAVILY_API_KEY=your_tavily_api_key

Run the server:
bash
```
python server.py
```

For Docker deployment, Claude Desktop integration, example usage, and troubleshooting, please refer to the full documentation.

Support & Contact

Website: gptr.dev
Email: [email protected]
GitHub: assafelovic/gptr-mcp :-)