xby-hackernews-search

HackerNews is one of the most活跃的 technical communities, yet asking an AI agent to track hot threads, filter high-signal posts, or drill into a comment tree hundreds of levels deep usually means hand-rolling API calls that are both brittle and slow to maintain. xby-hackernews-search wraps the full search, retrieval, and analysis pipeline behind the Model Context Protocol so agents can go from a natural-language intent to structured results in a single step.

The plugin exposes five core tools: search_posts combines keyword matching across titles, body text, and authors with tag filtering (story, poll, show_hn, ask_hn, front_page, author_*), numeric range conditions on points, num_comments, and created_at_i, and OR-logic tag grouping; get_front_page pulls the current ranked front-page stories; get_latest_posts returns the most recent posts in chronological order; get_item fetches any item by ID including the full nested comment tree and complete metadata; and get_user retrieves a profile with karma, bio, and account creation date. Every tool returns a unified paginated structure, making it easy for agents to page through results on demand.

It is built for engineers assembling AI agents, developers who need automated tech-trend monitoring, and any team that wants to fold HackerNews data into their agent workflows. Register for an API key, persist it once with set_xby_apikey, and the entire toolkit is available in subsequent conversations with no further configuration.