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Spot pre-launch products before they trend. Search the web and tech sites, extract and parse pages…
Spot pre-launch products before they trend. Search the web and tech sites, extract and parse pages…
Remote endpoints: streamable-http: https://server.smithery.ai/@rainbowgore/stealthee-mcp-tools/mcp
Valid MCP server (1 strong, 1 medium validity signals). 8 known CVEs in dependencies (0 critical, 2 high severity) Imported from the Official MCP Registry.
4 files analyzed · 8 issues found
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Available as Local & Remote
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From the project's GitHub README.

Stealthee is a dev-first system for surfacing pre-public product signals - before they trend. Built for CTOs and tech leaders who need competitive intelligence and early threat detection. It combines search, extraction, scoring, and alerting into a plug-and-play pipeline you can integrate into Claude, LangGraph, Smithery, or your own AI stack via MCP.
Perfect for competitive intelligence, technology trend monitoring, and strategic planning.
Use it if you're:
| Tool | Description |
|---|---|
web_search | Search the web for stealth launches (Tavily) |
url_extract | Extract content from URLs (BeautifulSoup) |
score_signal | AI-powered signal scoring (OpenAI) |
batch_score_signals | Batch process multiple signals |
search_tech_sites | Search tech news sites only |
parse_fields | Extract structured fields from HTML |
run_pipeline | End-to-end detection pipeline |
Clone and Setup
git clone https://github.com/rainbowgore/Stealthee-MCP-tools
cd stealthee-MCP-tools
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
Configure Environment
Fill the .env file with your API keys:
# Required
TAVILY_API_KEY=your_tavily_key_here
OPENAI_API_KEY=your_openai_key_here
NIMBLE_API_KEY=your_nimble_key_here
# Optional
SLACK_WEBHOOK_URL=your_slack_webhook_here
Start Servers
# MCP Server (for Claude Desktop)
python mcp_server_stdio.py
# FastMCP Server (for Smithery)
smithery dev
# FastAPI Server (Optional - Legacy)
python start_fastapi.py
All MCP tools listed above are available out-of-the-box in Smithery. Smithery is a visual agent and workflow builder for AI tools, letting you chain, test, and orchestrate these tools with no code.
To use Stealth Radar MCP in Cursor via the hosted URL (Streamable HTTP):
Stealth Radar (or any name you like).streamableHttp.https://smithery.ai/server/rainbowgore/Product-Stealth-Launch-Radar), orhttps://your-ngrok-url.ngrok-free.app/mcp (must end with /mcp).If you run the server locally, use stdio instead: set Type to stdio, Command to your Python path, and Args to mcp_server_stdio.py with cwd pointing at the repo.
Add to your Claude Desktop config.json file:
{
"mcpServers": {
"stealth-mcp": {
"command": "/path/to/stealthee-MCP-tools/.venv/bin/python",
"args": ["/path/to/stealthee-MCP-tools/mcp_server_stdio.py"],
"cwd": "/path/to/stealthee-MCP-tools",
"env": {
"TAVILY_API_KEY": "your_tavily_key",
"OPENAI_API_KEY": "your_openai_key"
}
}
}
}
For Analysts & Builders:
web_search: Find stealth product mentions across the weburl_extract: Pull and clean raw text from landing pagesscore_signal: Judge how likely a change log implies launchbatch_score_signals: Quickly triage dozens of scraped URLssearch_tech_sites: Limit queries to trusted domains onlyparse_fields: Extract pricing/release info from messy HTMLrun_pipeline: Full pipeline — search → extract → parse → scoreweb_search or search_tech_sites to find relevant URLsurl_extract to get clean content from URLsparse_fields to extract structured data (pricing, changelog, etc.)score_signal or batch_score_signals for AI-powered analysisYou can also run this project as a FastAPI server for REST-style access to all MCP tools.
Search for stealth launches:
curl -X POST "http://localhost:8000/tools/web_search" \
-H "Content-Type: application/json" \
-d '{"query": "stealth startup AI", "num_results": 5}'
Run full detection pipeline:
curl -X POST "http://localhost:8000/tools/run_pipeline" \
-H "Content-Type: application/json" \
-d '{"query": "new AI product launch", "num_results": 3}'
query (required): Search phrase (e.g. "AI roadmap")num_results (optional, default: 5): Number of search results to analyzetarget_fields (optional, default: ["pricing", "changelog"]): Fields to extract from HTMLThe score_signal and batch_score_signals tools use GPT-3.5 to evaluate:
| Field | Type | Description |
|---|---|---|
id | INTEGER | Primary key |
url | TEXT | Source URL |
title | TEXT | Signal title |
html_excerpt | TEXT | First 500 characters of content |
changelog | TEXT | Parsed changelog (optional) |
pricing | TEXT | Parsed pricing info (optional) |
score | REAL | Stealth likelihood (0–1) |
confidence | TEXT | Confidence level |
reasoning | TEXT | AI rationale for the score |
created_at | TEXT | ISO timestamp |
python start_fastapi.py
Then visit: http://localhost:8000/docs
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