Catalog›MCP servers›Web search & browser›ai.smithery/rainbowgore-stealthee-mcp-toolsASai.smithery/rainbowgore-stealthee-mcp-toolsSpot pre-launch products before they trend. Search the web and tech sites, extract and parse pages… Open site Open in the app JSON README (API)Spot pre-launch products before they trend. Search the web and tech sites, extract and parse pages…Security checkNeeds attention · Last check: 2026-10-29 · JSONNot a connector at this addressSignals are measured facts, not a safety guarantee. Watch this toolREADMELoading documentation…# Stealthee MCP - Tools for being early [](https://www.python.org/) [](https://fastapi.tiangolo.com/) [](https://github.com/nimbleai/mcp) [](https://platform.openai.com/) [](https://docs.tavily.com/) [](https://docs.nimbleai.dev/) [](https://slack.com/) [](https://smithery.tools/)  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 1. **Clone and Setup** ```bash 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: ```bash # 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** ```bash # 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](https://smithery.ai/server/rainbowgore/Product-Stealth-Launch-Radar). 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 [Stealthee MCP Tools page on Smithery](https://smithery.ai/server/rainbowgore/Product-Stealth-Launch-Radar).** 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. ### Cursor (Stealth Radar MCP) To use **Stealth Radar MCP** in Cursor via the hosted URL (Streamable HTTP): 1. Open **Cursor Settings** → **MCP** (or search for "MCP" in settings). 2. 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`). 3. 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: ```json { "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 - **Swagger UI**: [http://localhost:8000/docs](http://localhost:8000/docs) - **Health Check**: [http://localhost:8000/health](http://localhost:8000/health) - **Tool Manifest**: [http://localhost:8000/tools](http://localhost:8000/tools) --- ### Example Usage **Search for stealth launches:** ```bash 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:** ```bash 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) ```bash python start_fastapi.py ``` Then visit: [http://localhost:8000/docs](http://localhost:8000/docs) --- Built with 💜 for those who spot what others miss. DetailsTypeMCP serversTopicWeb search & browserPublisherai.smitherySourceofficialCategoryToolsWhere it runsOnlineMCP endpointhttps://server.smithery.ai/@rainbowgore/stealthee-mcp-tools/mcpVersion1.14.0Last update2026-04-05T17:18:38ZProject statusActiveLanguagePythonLicenseMITAdded2026-08-29 03:00:41Updated2026-08-29 03:00:41Source idai.smithery/rainbowgore-stealthee-mcp-toolsMoreMore mcp servers on Web search & browserAll from ai.smitheryA–Z index
