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.
What can you do with 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.jsonfor npm publishing - ✅ CHANGELOG tracked
Install
The installation steps are in the documentation below. Documentation
Documentation
From the project's README on GitHub, refreshed weekly. Links open on 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, 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 | text | full, extractMainOnly` |
extract_insights | Entities, stats regex, key-point scoring, reading time | content, goal? | ||
deep_research | Power tool — search → parallel scrape → synthesize report | `topic, depth=quick | standard | deep, maxSources, saveMemory` |
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 findingsresearch://history— last 100 actionsresearch://stats— cache size, uptime
Prompts:
deep-dive-research— full research workflowfact-check— fact-checker squadcompare-narratives— bias & comparison table
Quickstart & Installation
⚡ 1-Click Auto Setup for Claude Desktop (Recommended)
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 findingshistory.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.jsonfor 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.
Frequently asked questions
What is 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.
How do I install MCP Deep Research Server?
Follow the installation steps in its documentation, shown on this page and in its repository.
Is MCP Deep Research Server open source?
Yes. Its code is on GitHub under the MIT license.
Who maintains MCP Deep Research Server?
It is published by SECRET4422. AI.tn lists it but does not maintain or endorse it.
From the community awesome-mcp-servers lists and the project's GitHub repository. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.