How I Built an Autonomous AI Tool Engine with MCP, Llama 3.3, and Automated Hugging Face Pipelines

Short summary

The author built AI Tool Hunter V2, an autonomous discovery engine that scans the web for new AI tools, extracts metadata via Llama 3.3-70B on Groq, and exposes results through a Model Context Protocol server. The system uses Tavily for live web scanning, Supabase for storage with row-level security, and publishes an open-source dataset on Hugging Face. The post details the ingestion pipeline, data flywheel, and MCP architecture with full tech stack and GitHub links.

• •Autonomous pipeline uses Tavily API + Llama 3.3-70B on Groq for web scanning and intent-based metadata extraction
• •System exposes data via Model Context Protocol (MCP) server for AI agent consumption
• •Open-source dataset and GitHub repo published for community reuse

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