Deep Research MCP Server

<p align="center"> <img src="assets/logo.png" width="140" alt="Deep Research MCP Logo" /> </p>

<h1 align="center">Deep Research MCP Server</h1> <p align="center"><b>Non-generic MCP for real research. Search → Scrape → Synthesize → Fact-check → Remember.</b></p>

<p align="center"> <a href="https://github.com/SECRET4422/mcp-deep-research-server/actions"><img src="https://img.shields.io/github/actions/workflow/status/SECRET4422/mcp-deep-research-server/ci.yml?branch=main&label=CI&logo=github" alt="CI" /></a> <a href="LICENSE"><img src="https://img.shields.io/github/license/SECRET4422/mcp-deep-research-server" alt="MIT" /></a> <a href="package.json"><img src="https://img.shields.io/badge/TypeScript-5.6-blue?logo=typescript" alt="TS" /></a> <a href="https://modelcontextprotocol.io"><img src="https://img.shields.io/badge/MCP-Compatible-black?logo=anthropic" alt="MCP" /></a> <a href="https://www.npmjs.com/package/mcp-deep-research-server"><img src="https://img.shields.io/badge/npm-1.1.0-red?logo=npm" alt="npm" /></a> <img src="https://img.shields.io/badge/No%20API%20Key%20Needed-green" alt="No API Key" /> </p>

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Why not generic?

Generic MCP (boring)	This MCP (pro)
echo, fetch	Orchestrated deep research
Returns raw HTML	Cheerio + Turndown → clean markdown + headings, links, meta
No memory	Persistent memory in ~/.mcp-deep-research/
One page at a time	Parallel 3-worker scraper, 10min cache
No reasoning	Fact-check with stance scoring, contradiction detection

Architecture

graph LR
    A[User: deep_research topic] --> B[search_web DDG HTML]
    B --> C[Parallel Scrape x3-8]
    C --> D[cheerio clean + turndown md]
    D --> E[extract_insights heuristic]
    E --> F[Synthesize Report + Citations]
    F --> G[memory_save + history]
    F --> H[Return to Claude]
    
    I[compare_sources] --> C
    J[fact_check_claim] --> B
    K[memory_search] --> G

Tools (8)

Tool	What it does	Params
search_web	DuckDuckGo HTML search, no API key, UDDG decode	query, count 1-10, timeFilter
scrape_page	Fetch + main-content heuristic + markdown	`url, format=markdown
extract_insights	Entities, stats regex, key-point scoring, reading time	content, goal?
deep_research	Power tool — search → parallel scrape → synthesize report	`topic, depth=quick
compare_sources	2-5 URLs → consensus vs unique vs contradictions	urls[], focus?
fact_check_claim	Searches support + debunked OR false, heuristic verdict	claim, searchDepth
memory_save	Save finding to JSON, survives restarts	key, value, tags[], source?
memory_search	Fuzzy search in persistent memory	query, tags[], limit

Resources:

• research://memory — all saved findings
• research://history — last 100 actions
• research://stats — cache size, uptime

Prompts:

• deep-dive-research — full research workflow
• fact-check — fact-checker squad
• compare-narratives — bias & comparison table

Install

git clone https://github.com/SECRET4422/mcp-deep-research-server.git
cd mcp-deep-research-server
npm install
npm run build

Test (smoke)

npm run test:mcp
# or
npm run inspect # opens http://localhost:6274

Manually tested:

[search] Dehradun → 3 results ✓
[deep_research] What is MCP → 3 sources in 2.1s ✓
tools/list → 8 tools ✓

Add to Claude Desktop

Edit config:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "deep-research": {
      "command": "node",
      "args": ["/absolute/path/to/mcp-deep-research-server/build/index.js"]
    }
  }
}

Restart Claude Desktop.

Add to Cursor / Windsurf / VS Code

.cursor/mcp.json or mcp.json:

{
  "mcpServers": {
    "deep-research": {
      "command": "node",
      "args": ["./build/index.js"],
      "cwd": "/path/to/mcp-deep-research-server"
    }
  }
}

Example Prompts

Deep Research:

Use deep_research to research "Best LLM fine-tuning in 2026, depth deep" then compare LoRA vs QLoRA

Fact Check:

Fact check claim: "Bun is faster than Node" using fact_check_claim

Compare:

Compare these 3 URLs about MCP architecture focusing on security: https://modelcontextprotocol.io/docs/getting-started/intro https://www.anthropic.com/news/model-context-protocol https://en.wikipedia.org/wiki/Model_Context_Protocol

See examples/claude-example.md for more.

Data Storage

All in ~/.mcp-deep-research/:

• memory.json — persistent findings
• history.json — audit log (100 max)
• cache/ — reserved

No DB, no external calls except search/scrape.

Pro Features in v1.1.0

• ✅ Logo + pro README + badges
• ✅ GitHub Actions CI (Node 18/20/22) + Release workflow
• ✅ Issue templates, PR template, CONTRIBUTING, SECURITY
• ✅ .editorconfig, smoke test script
• ✅ Optimized package.json for npm publishing
• ✅ CHANGELOG tracked

Roadmap

• [ ] Tavily / Brave API fallback if keys present
• [ ] PDF parsing via pdf-parse
• [ ] YouTube transcript tool
• [ ] Vector search on memory (embeddings)
• [ ] Blocklist for SSRF (169.254.169.254 etc)
• [ ] Smithery registry

Dev

npm run dev     # tsx watch
npm run build
npm run lint

Guidelines in CONTRIBUTING.md.

License

MIT © SECRET4422 — See LICENSE

Built with 🧠 for Dehradun → World. Not a generic MCP.