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Deep Research MCP Server

@u14app

RepositoryHomepage

Basic information

Category

Data & Analytics

License

MIT

Transports

stdio

Author

u14app

About Deep Research MCP Server

Use any LLMs (Large Language Models) for Deep Research. Support SSE API and MCP server.

#gemini#openai#grok#anthropic#ollama#deepseek#mcp-server#deep-research#deepresearch

Deep Research MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "deep-research": {
      "command": "docker",
      "args": [
        "pull",
        "xiangfa/deep-research:latest"
      ]
    }
  }
}

Deep Research MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Deep Research?

Deep Research is an open-source AI-powered tool that generates in-depth research reports in about two minutes. It supports multiple large language models and search engines, processes and stores all data locally in the browser to protect user privacy, and can be deployed on Vercel, Cloudflare, Docker, or as a static site.

How to use Deep Research?

Use the free Gemini model by obtaining a Gemini API key, then deploy the project with a one-click button to Vercel or Cloudflare, and start using. For other LLMs, set the LLM API key and optional API base URL after deployment. For local development, clone the repository, install dependencies with pnpm, set environment variables via env.tpl, and run pnpm dev.

Key features of Deep Research

  • Rapid deep research reports in about two minutes.
  • Privacy-focused: all data stored locally in the browser.
  • Supports multiple LLMs including Gemini, OpenAI, Anthropic, Deepseek, etc.
  • Supports multiple web search engines like Tavily, Firecrawl, Searxng, etc.
  • Offers both local API and server-side API (SSE and MCP).
  • Includes local knowledge base, artifact editing, and knowledge graph features.

Use cases of Deep Research

  • Quickly generate comprehensive research reports on any topic.
  • Perform iterative research by refining or adjusting at any stage.
  • Use as a SaaS service via its SSE API or integrate with other AI tools via the MCP server.
  • Build a local knowledge base from uploaded text, Office, and PDF files.

Frequently asked questions

1

How does Deep Research protect my privacy?

All data is processed and stored locally in your browser. When using the local API mode, no API keys or server-side configurations are needed.

2

Which AI models and search engines does Deep Research support?

It supports Gemini, OpenAI, Anthropic, Deepseek, Atlas Cloud, Grok, Mistral, Azure OpenAI, OpenAI-compatible LLMs, OpenRouter, Ollama, etc. For search, it supports Searxng, Tavily, Firecrawl, fastCRW, Exa, Bocha, Brave, and others.

3

Can I deploy Deep Research myself?

Yes, you can deploy it on Vercel, Cloudflare Pages, using Docker, or as a static export. Detailed guides are provided for each method.

4

Does Deep Research support APIs for integration with other tools?

Yes, it provides a Server-Sent Events (SSE) API and a Model Context Protocol (MCP) server. You can use the SSE API to start research tasks and receive real-time results.

5

Is the project open-source?

Yes, Deep Research is MIT licensed and freely available for personal and commercial use.

Comments

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