MCP Deep Research Server

Communauté Recherche et web

Autonomous multi-source deep research with DuckDuckGo search (zero API keys), parallel scrapers, markdown cleaning, synthesis with citations, fact-checking, and persistent memory.

Que pouvez-vous faire avec MCP Deep Research Server ?

  • ✅ 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

Installation

Les étapes d'installation sont dans la documentation ci-dessous. Documentation

Documentation

Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.

Deep Research MCP Server

Non-generic MCP for real research. Search → Scrape → Synthesize → Fact-check → Remember.


Why not generic?
Generic MCP (boring)This MCP (pro)
echo, fetchOrchestrated deep research
Returns raw HTMLCheerio + Turndown → clean markdown + headings, links, meta
No memoryPersistent memory in ~/.mcp-deep-research/
One page at a timeParallel 3-worker scraper, 10min cache
No reasoningFact-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)
ToolWhat it doesParams
search_webDuckDuckGo HTML search, no API key, UDDG decodequery, count 1-10, timeFilter
scrape_pageFetch + main-content heuristic + markdown`url, format=markdowntextfull, extractMainOnly`
extract_insightsEntities, stats regex, key-point scoring, reading timecontent, goal?
deep_researchPower tool — search → parallel scrape → synthesize report`topic, depth=quickstandarddeep, maxSources, saveMemory`
compare_sources2-5 URLs → consensus vs unique vs contradictionsurls[], focus?
fact_check_claimSearches support + debunked OR false, heuristic verdictclaim, searchDepth
memory_saveSave finding to JSON, survives restartskey, value, tags[], source?
memory_searchFuzzy search in persistent memoryquery, 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
Quickstart & Installation

Run this single command in your terminal:

npx mcp-deep-research-server --install

That's it! It automatically detects your OS, writes the config to your claude_desktop_config.json, and configures the 8 research tools. Just restart Claude Desktop.


📦 Install via Smithery

To automatically install for Claude Desktop or Cursor via Smithery:

npx -y @smithery/cli install mcp-deep-research-server --client claude

🛠️ Manual / Other Clients Setup

Run via npx:

npx mcp-deep-research-server

Or build from source:

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

🤖 Agent Skill (Claude Code / Antigravity / Cursor / Agent Frameworks)

This repository includes an open-standard Agent Skill that teaches AI coding assistants when and how to autonomously run deep research workflows.

  • Import via skill directory: skills/deep-research/
  • Spec compliant with Claude Code, Cursor, and Google Antigravity agents.
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)
  • [x] SSRF protection — private/internal IP blocklist for scrape_page
  • [x] Smithery & Glama registry integration
Dev
npm run dev     # tsx watch
npm run build
npm run lint

Guidelines in CONTRIBUTING.md.

License

MIT © Prabhakar Pal — See LICENSE

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

Questions fréquentes

Qu'est-ce que MCP Deep Research Server ?

Autonomous multi-source deep research with DuckDuckGo search (zero API keys), parallel scrapers, markdown cleaning, synthesis with citations, fact-checking, and persistent memory.

Comment installer MCP Deep Research Server ?

Suivez les étapes d'installation de sa documentation, affichée sur cette page et dans son dépôt.

MCP Deep Research Server est-il open source ?

Oui. Son code est sur GitHub sous licence MIT.

Qui maintient MCP Deep Research Server ?

Il est publié par SECRET4422. AI.tn le référence sans le maintenir ni le recommander.

Issu des listes communautaires awesome-mcp-servers et du dépôt GitHub du projet. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.