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Stealthee MCP

by rainbowgore

Stealthee MCP - Tools for being early

Python FastAPI MCP OpenAI API Tavily Nimble Slack Alerts Smithery

Stealthee Logo

Stealthee is a dev-first system for surfacing pre-public product signals - before they trend. 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.

Use it if you're:

  • 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

  1. 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
  2. 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
  3. 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

  1. Open the

  2. Click "Try in Playground" to test any tool interactively.

  3. Use the visual workflow builder to chain tools together (e.g., search → extract → score).

  4. 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 web

  • url_extract: Pull and clean raw text from landing pages

  • score_signal: Judge how likely a change log implies launch

  • batch_score_signals: Quickly triage dozens of scraped URLs

  • search_tech_sites: Limit queries to trusted domains only

  • parse_fields: Extract pricing/release info from messy HTML

  • run_pipeline: Full pipeline — search → extract → parse → score

🔬 Signal Intelligence Workflow

  1. Search Phase: Use web_search or search_tech_sites to find relevant URLs

  2. Extraction Phase: Use url_extract to get clean content from URLs

  3. Parsing Phase: Use parse_fields to extract structured data (pricing, changelog, etc.)

  4. Analysis Phase: Use score_signal or batch_score_signals for AI-powered analysis

  5. Storage Phase: All signals are stored in SQLite database

  6. 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


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 analyze

  • target_fields (optional, default: ["pricing", "changelog"]): Fields to extract from HTML


What run_pipeline Does

  1. Searches tech and stealth-friendly sources using Tavily

  2. Extracts raw content from each result

  3. Parses structured signals (pricing, changelog, etc.)

  4. Scores each result with OpenAI to estimate stealthiness

  5. Stores results in local SQLite

  6. 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.

-
security - not tested
A
license - permissive license
-
quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables detection and analysis of pre-public product launches through web search, content extraction, AI-powered scoring, and automated alerting. Provides comprehensive tools for surfacing stealth startup signals before they trend publicly.

  1. What's cookin'?
    1. MCP Tools
  2. Installation & Setup
    1. Prerequisites
    2. Quick Start
  3. Smithery & Claude Desktop Integration
    1. Available Tools
    2. How to Use in Smithery
    3. Claude Desktop Integration
  4. Tool Use Cases
    1. 🔬 Signal Intelligence Workflow
      1. ⚙️ FastAPI Server
        1. Base Endpoints
        2. Example Usage
        3. Pipeline Parameters
        4. What run_pipeline Does
        5. AI Scoring Logic
        6. Database Schema (data/signals.db)
        7. Dev Quickstart (FastAPI)

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