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
```mermaid
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 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
#### ⚡ 1-Click Auto Setup for Claude Desktop (Recommended)
Run this single command in your terminal:
```bash
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:
```bash
npx -y @smithery/cli install mcp-deep-research-server --client claude
```
---
#### 🛠️ Manual / Other Clients Setup
**Run via npx:**
```bash
npx mcp-deep-research-server
```
**Or build from source:**
```bash
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](skills/deep-research/SKILL.md) 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)
```bash
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`
```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`:
```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
```bash
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.