pminervini/deep-research-mcp
by Various
MCP server for OpenAI's Deep Research APIs, Gemini Deep Research Agent, Allen AI's DR-Tulu, and Hugging Face's Open Deep Research
MCP
pminervini/deep-research-mcp
Added 1 June 2026
Overview
An MCP server that provides a unified interface for multiple deep research AI tools including OpenAI's Deep Research APIs, Gemini Deep Research Agent, Allen AI's DR-Tulu, and Hugging Face's Open Deep Research. It is written in Python and implements the Model Context Protocol to enable integration with compatible applications. Developers can route requests to different research models through a single MCP endpoint.
Best for
Best for
Developers building MCP-based tools that need flexible access to multiple deep research AI models
Use cases
- Connecting MCP-compatible applications to OpenAI Deep Research APIs
- Switching between Gemini Deep Research Agent and open-source alternatives via one server
- Building research automation tools that aggregate results from multiple deep research models
Notes
An MCP server that provides a unified interface for multiple deep research AI tools including OpenAI’s Deep Research APIs, Gemini Deep Research Agent, Allen AI’s DR-Tulu, and Hugging Face’s Open Deep Research. It is written in Python and implements the Model Context Protocol to enable integration with compatible applications. Developers can route requests to different research models through a single MCP endpoint.
84 stars on GitHub. Last updated 2026-05-13. Licensed MIT.
Use cases
- Connecting MCP-compatible applications to OpenAI Deep Research APIs
- Switching between Gemini Deep Research Agent and open-source alternatives via one server
- Building research automation tools that aggregate results from multiple deep research models
Pros
- Unifies access to proprietary and open-source deep research APIs
- Open-source Python implementation enables customization and self-hosting
- Supports multiple providers without changing client code
Cons
- Relatively low community adoption with 84 GitHub stars
- Requires MCP-compatible clients or adapters to use
- Each provider requires its own API keys and may have separate rate limits
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Unifies access to proprietary and open-source deep research APIs
- Open-source Python implementation enables customization and self-hosting
- Supports multiple providers without changing client code
Cons
- Relatively low community adoption with 84 GitHub stars
- Requires MCP-compatible clients or adapters to use
- Each provider requires its own API keys and may have separate rate limits
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