article-research-mcp
Article and tutorial research MCP for agent planning — dev.to, deduped ranked briefs, deep-research hooks
Key Value Proposition
Delivers deduped, ranked research briefs for agent planning directly in the MCP loop — no context switching to search UIs.
Summary
article-research-mcp is an MCP server that streamlines article and tutorial discovery during AI agent planning. It searches dev.to and Hacker News, deduplicates results, and produces ranked topic briefs aligned with Hermes deep-research methodology. Developers and agent authors can run it locally with uv and surface methodology, research plans, and briefs as MCP resources.
Proof Points
- Multi-source search across dev.to, Hacker News (Algolia), and configurable RSS/Atom feeds, with devto+hn as defaults
- Optional LLM-assisted ranking via any OpenAI-compatible API (e.g., local Ollama), with deterministic scoring as the fallback
- Seven planning tools including plan_article_research, search_articles, build_topic_brief, and get_article, plus cron-driven daily briefs
About
Article and tutorial research MCP for agent planning — dev.to, briefs, dedupe, deep-research hooks
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