gptr-mcp — MCP Server by assafelovic

by assafelovic · MCP Server · ★ 327

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About gptr-mcp

🔍 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 Deskt

deep-researchdeepresearchgpt-researchermcpmcp-serverwebsearch

Quick Facts

Stars327
Forks53
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score51.2/100
Open Issues14
Last Updated2025-11-07
Created2025-03-30
Platformsbrowser, mcp, python
Est. Tokens~2k

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Frequently Asked Questions

What is gptr-mcp?

gptr-mcp is MCP server for enabling LLM applications to perform deep research via the MCP protocol. It is categorized as a MCP Server with 327 GitHub stars.

What programming language is gptr-mcp written in?

gptr-mcp is primarily written in Python. It covers topics such as deep-research, deepresearch, gpt-researcher.

How do I install or use gptr-mcp?

You can find installation instructions and usage details in the gptr-mcp GitHub repository at github.com/assafelovic/gptr-mcp. The project has 327 stars and 53 forks, indicating an active community.

What license does gptr-mcp use?

gptr-mcp is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to gptr-mcp?

The top alternatives to gptr-mcp on Agent Skills Hub include MCPBench, SearChat, pydantic-deepagents. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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