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.
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
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