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

Most AI tool directories suffer from two issues: they are statically hardcoded, and they break when you search by intent instead of exact keywords.

I built AI Tool Hunter V2 to solve this—turning a basic directory into an automated discovery engine, open-source dataset pipeline, and Model Context Protocol (MCP) server.

Here is a complete breakdown of the system architecture and the engineering trade-offs made along the way.

1. The Autonomous Ingestion Pipeline

Rather than manually curating links, a daily GitHub Actions workflow (harvest.yml) triggers an ingestion script: