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Deep Research Server

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An MCP server for deep research that performs search, scraping, synthesis, fact-checking, and persistent memory, enabling users to conduct comprehensive researc

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Описание

An MCP server for deep research that performs search, scraping, synthesis, fact-checking, and persistent memory, enabling users to conduct comprehensive research tasks via Claude.

README

Deep Research MCP Logo

Deep Research MCP Server

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

CI MIT TS MCP npm No API Key


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.

from github.com/SECRET4422/mcp-deep-research-server

Установить Deep Research Server в Claude Desktop, Claude Code, Cursor

Рекомендуется · одна команда, все IDE
unyly install deep-research-mcp-server

Ставит в Claude Desktop, Claude Code, Cursor и VS Code — сам разбирается с npx, uvx и сборкой из исходников.

Впервые? Поставь CLI: curl -fsSL https://unyly.org/install | sh

Или настроить вручную

Выполни в терминале:

claude mcp add deep-research-mcp-server -- npx -y github:SECRET4422/mcp-deep-research-server

FAQ

Deep Research Server MCP бесплатный?

Да, Deep Research Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Deep Research Server?

Нет, Deep Research Server работает без API-ключей и переменных окружения.

Deep Research Server — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Deep Research Server в Claude Desktop, Claude Code или Cursor?

Открой Deep Research Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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