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GPTR

v1.0.0•Search & Data Extraction•stable

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

deep-researchdeepresearchgpt-researchermcpmcp-server
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What is GPTR?

GPTR is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to mcp server for enabling llm applications to perform deep research via the mcp protocol

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

This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.

Features

  • MCP server for enabling LLM applications to perform deep res

Use Cases

Enable deep research via MCP.
Validate findings across sources.
Perform comprehensive web analysis.
assafelovic

Maintainer

LicenseMIT
Languagepython
Versionv1.0.0
UpdatedMay 8, 2026
Statushealthy
Maintenanceactive

Works with

ClaudeOpenAIwindowsmacoslinux
View SourceBrowse All Servers

Installation

Manual Installation

npx gptr

Configuration

Configuration Details

Config File

claude_desktop_config.json

Performance

Response Metrics

Response Time< 200ms
ThroughputMedium

Resource Usage

Memory UsageLow
CPU UsageLow

How to Set Up and Use GPTR

GPTR (GPT Researcher MCP) is the MCP server interface for the GPT Researcher framework, enabling AI assistants to perform deep, multi-source autonomous web research through the Model Context Protocol. Rather than returning a single search result, it orchestrates dozens of web queries, validates findings across multiple trusted sources, and synthesizes the results into well-cited research reports. It exposes six specialized tools covering comprehensive deep research, quick searches, report writing, and source retrieval — making it ideal for analysts and developers who need Claude to produce high-quality, factually grounded research outputs.

Prerequisites

  • Python 3.11 or newer
  • An OpenAI API key (OPENAI_API_KEY) for language model inference
  • A Tavily API key (TAVILY_API_KEY) for web search — sign up at https://tavily.com
  • An MCP client such as Claude Desktop
1

Clone the gptr-mcp repository

Clone the GPT Researcher repository and navigate to the gptr-mcp subdirectory that contains the MCP server.

git clone https://github.com/assafelovic/gpt-researcher.git
cd gpt-researcher/gptr-mcp
2

Install dependencies

Install the Python packages required by the gptr-mcp server, including gpt-researcher and its dependencies.

pip install -r requirements.txt
3

Configure API keys

Create a .env file with your OpenAI and Tavily API keys. Both are required — OPENAI_API_KEY drives the synthesis and TAVILY_API_KEY powers web search.

cp .env.example .env
# Edit .env:
# OPENAI_API_KEY=sk-...
# TAVILY_API_KEY=tvly-...
4

Add the server to your MCP client

Add an entry in your Claude Desktop config file. API keys must be in the env block because the server subprocess does not inherit your shell environment.

{
  "mcpServers": {
    "gptr": {
      "command": "python",
      "args": ["/absolute/path/to/gpt-researcher/gptr-mcp/server.py"],
      "env": {
        "OPENAI_API_KEY": "sk-your-openai-key",
        "TAVILY_API_KEY": "tvly-your-tavily-key"
      }
    }
  }
}
5

Restart Claude Desktop and test

Restart Claude Desktop and ask it to research a topic. The deep_research, quick_search, and write_report tools should be available.

GPTR Examples

Client configuration

Claude Desktop configuration for the GPTR MCP server. Use absolute paths for the server.py argument.

{
  "mcpServers": {
    "gptr": {
      "command": "python",
      "args": ["/Users/yourname/gpt-researcher/gptr-mcp/server.py"],
      "env": {
        "OPENAI_API_KEY": "sk-your-openai-api-key",
        "TAVILY_API_KEY": "tvly-your-tavily-api-key"
      }
    }
  }
}

Prompts to try

Use these prompts to invoke GPTR research capabilities via Claude.

- "Use deep_research to investigate the latest advances in solid-state battery technology"
- "Quick search: what are the top open-source LLM models available as of 2025?"
- "Research the economic impact of remote work on urban real estate markets and write a full report"
- "Get the research context from your last search and identify the three most credible sources"
- "Research regulatory changes in EU AI legislation in 2024-2025 and summarize key compliance requirements"

Troubleshooting GPTR

ImportError or missing module when starting server.py

Ensure you installed dependencies from within the gptr-mcp directory: 'cd gpt-researcher/gptr-mcp && pip install -r requirements.txt'. If using a virtual environment, activate it before running pip install.

Research hangs or times out on deep_research

Deep research queries many sources and can take several minutes. If it consistently times out, check your Tavily API rate limits and ensure OPENAI_API_KEY has sufficient quota. Reduce research scope by using quick_search for faster results.

API keys not found despite being in .env

Claude Desktop does not load .env files when launching subprocess servers. Move the API keys into the 'env' block of the MCP JSON configuration, as shown in the client configuration example above.

Frequently Asked Questions about GPTR

What is GPTR?

GPTR is a Model Context Protocol (MCP) server that mcp server for enabling llm applications to perform deep research via the mcp protocol It connects AI assistants to external tools and data sources through a standardized interface.

How do I install GPTR?

Follow the installation instructions on the GPTR GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.

Which AI clients work with GPTR?

GPTR works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.

Is GPTR free to use?

Yes, GPTR is open source and available under the MIT license. You can use it freely in both personal and commercial projects.

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Quick Config Preview

{ "mcpServers": { "gptr": { "command": "npx", "args": ["-y", "gptr"] } } }

Add this to your claude_desktop_config.json or .cursor/mcp.json

Read the full setup guide →

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