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:
2. The Open-Source Data Flywheel
To share this corpus with the open-source community, the daily cron pipeline doesn't stop at database insertion.
It formats the updated database into a structured corpus and uses huggingface_hub to push a nightly commit directly to Hugging Face (AI-Tools-Corpus-2026).
3. Exposing the System via MCP (Model Context Protocol)
Modern developer tools live in the IDE. I built a dedicated MCP server hosted on Render so external LLMs (in Cursor, VS Code, or Claude Desktop) can call the search index natively.
Tech Stack
Links & Open Source: