Loading…
My profolio full-stack AI agent demo, built end-to-end with Next.js, TypeScript, Node.js, and PostgreSQL. MCP-backed chat interface for natural-language CRUD; BullMQ async worker using LLMs for structured data extraction from external HTML pages.
price-monitoring-agent is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 49/100. My profolio full-stack AI agent demo, built end-to-end with Next.js, TypeScript, Node.js, and PostgreSQL. MCP-backed chat interface for natural-language CRUD; B. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding price-monitoring-agent to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/onlineeric/price-monitoring-agent.gitReview the upstream source before running this command.
Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "onlineeric/price-monitoring-agent"
}My profolio full-stack AI agent demo, built end-to-end with Next.js, TypeScript, Node.js, and PostgreSQL. MCP-backed chat interface for natural-language CRUD; BullMQ async worker using LLMs for structured data extraction from external HTML pages.