ai.smithery/rainbowgore-stealthee-mcp-tools
Spot pre-launch products before they trend. Search the web and tech sites, extract and parse pages…
Ask AI about ai.smithery/rainbowgore-stealthee-mcp-tools
Powered by Claude · Grounded in docs
I know everything about ai.smithery/rainbowgore-stealthee-mcp-tools. Ask me about installation, configuration, usage, or troubleshooting.
0/500
Reviews
Documentation
Stealthee MCP - Tools for being early

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:
- A CTO or tech leader needing competitive intelligence, early threat detection, and innovation scouting to inform strategic decisions
- An investor hunting for pre-traction signals
- A founder scanning for competitors before launch
- A researcher tracking emerging markets
- A developer building agents, dashboards, or alerting tools that need fresh product intel.
What's cookin'?
MCP Tools
| 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 |
Installation & Setup
Prerequisites
- API keys for external services (see Environment Variables)
Quick Start
-
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
.envfile 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
Smithery & Claude Desktop Integration
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.
Available Tools
- web_search: Search the web for stealth launches using Tavily.
- url_extract: Extract and clean content from any URL.
- score_signal: Use OpenAI to score a single signal for stealthiness.
- batch_score_signals: Score multiple signals in one go.
- search_tech_sites: Search only trusted tech news sources.
- parse_fields: Extract structured fields (like pricing, changelog) from HTML.
- run_pipeline: End-to-end pipeline: search, extract, parse, score, and store.
How to Use in Smithery
- Open the Stealthee MCP Tools page on Smithery.
- Click "Try in Playground" to test any tool interactively.
- Use the visual workflow builder to chain tools together (e.g., search → extract → score).
- Integrate with Claude Desktop or your own agents by copying the workflow or using the API endpoints provided by Smithery.
Cursor (Stealth Radar MCP)
To use Stealth Radar MCP in Cursor via the hosted URL (Streamable HTTP):
- Open Cursor Settings → MCP (or search for "MCP" in settings).
- Under Install MCP Server, fill in:
- Name:
Stealth Radar(or any name you like). - Type:
streamableHttp. - URL: Use either:
- Smithery: The connection URL from your server's Smithery Connect page (e.g.
https://smithery.ai/server/rainbowgore/Product-Stealth-Launch-Radar), or - Direct: Your server's MCP endpoint, e.g.
https://your-ngrok-url.ngrok-free.app/mcp(must end with/mcp).
- Smithery: The connection URL from your server's Smithery Connect page (e.g.
- Name:
- Click Install. Cursor will connect to the server; once added, it loads automatically when you use Cursor.
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.
Claude Desktop Integration
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"
}
}
}
}
Tool Use Cases
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 → score
Signal Intelligence Workflow
- Search Phase: Use
web_searchorsearch_tech_sitesto find relevant URLs - Extraction Phase: Use
url_extractto get clean content from URLs - Parsing Phase: Use
parse_fieldsto extract structured data (pricing, changelog, etc.) - Analysis Phase: Use
score_signalorbatch_score_signalsfor AI-powered analysis - Storage Phase: All signals are stored in SQLite database
- Alert Phase: High-confidence signals trigger Slack notifications
FastAPI Server
You can also run this project as a FastAPI server for REST-style access to all MCP tools.
Base Endpoints
- Swagger UI: http://localhost:8000/docs
- Health Check: http://localhost:8000/health
- Tool Manifest: http://localhost:8000/tools
Example Usage
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}'
Pipeline Parameters
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 HTML
What run_pipeline Does
- Searches tech and stealth-friendly sources using Tavily
- Extracts raw content from each result
- Parses structured signals (pricing, changelog, etc.)
- Scores each result with OpenAI to estimate stealthiness
- Stores results in local SQLite
- Notifies via Slack if confidence is high
AI Scoring Logic
The score_signal and batch_score_signals tools use GPT-3.5 to evaluate:
- Stealth indicators (e.g. private changelogs, missing press, beta flags)
- Confidence level (Low / Medium / High)
- Textual reasoning (used in UI or alerting)
Database Schema (data/signals.db)
| 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 |
Dev Quickstart (FastAPI)
python start_fastapi.py
Then visit: http://localhost:8000/docs
Built with 💜 for those who spot what others miss.
