oss-trends-mcp
Queries GitHub repositories, star momentum, forks, licenses, topics, and repository health signals to discover and rank trending open-source projects.
Retrieves npm package download counts and growth trends to assess adoption of open-source projects in the JavaScript/Node.js ecosystem.
Retrieves PyPI package download counts and growth trends to assess adoption of open-source projects in the Python ecosystem.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@oss-trends-mcprecommend trending open-source libraries for building a RAG pipeline"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
oss-trends-mcp
Task-driven trending-OSS recommender for AI builders, exposed as an MCP server. Ask "what OSS is trending for building X?" and get ranked open-source projects backed by live signals: GitHub star momentum, npm/PyPI download growth, and Hacker News buzz.
Awesome lists go stale in weeks; the AI tooling landscape moves in days. oss-trends-mcp ranks candidates at question time from real signals instead of hand-curated links — and separates established players from rising newcomers (repos younger than 18 months).
Tools
recommend_oss (main)
Inputs: task (what you want to build), queries (focused GitHub search terms — recommended; expanded from the task text otherwise), ecosystem (auto/npm/pypi/none), limit. Returns an established ranking plus risingNewcomers, each with a transparent score breakdown so the calling LLM can explain the "why". The LLM expands the task into search queries; the server does deterministic collection and scoring.
search_repos
Raw GitHub repository search with normalized output.
repo_health
Deep signals for one repository: star momentum, maintenance freshness, npm/PyPI adoption, HN buzz, and a 0-100 score.
compare_repos
Side-by-side trend comparison of 2-6 repositories, ranked by composite score.
Related MCP server: Dev AI Ecosystem MCP
Scoring (0-100)
momentum 35 — stars gained in the last 30 days (log scale)
maintenance 20 — push freshness decay; archived repos score 0
adoption 20 — npm or PyPI monthly downloads plus growth bonus (last 28d vs previous 28d)
community 15 — forks, license, topics
buzz 10 — Hacker News stories and top score over the last 180 days
Setup
Build and register with Claude Code:
npm install && npm run build
claude mcp add oss-trends -s user -- node /absolute/path/to/oss-trends-mcp/dist/index.jsRecommended: give the server a GITHUB_TOKEN. Without it GitHub allows 10 searches/min and 60 core requests/hour, so star-momentum sampling often degrades to fallback signals:
claude mcp add oss-trends -s user -e GITHUB_TOKEN=ghp_xxx -- node /absolute/path/to/dist/index.jsWorks in any MCP client (Claude Code, Cursor, etc.) via stdio.
Limitations
Repos above ~40k stars: the stargazers API caps at 400 pages, so recent star history is unreachable; momentum falls back to age-adjusted average velocity plus npm growth.
Star velocity for other large repos is a sampled lower bound (last pages x 100 stars).
npm/PyPI package matching is heuristic (repository URL match); unmatched repos simply skip adoption signals.
Development
npm run build, npm test, npm run lint. A live end-to-end check drives the compiled server over stdio and calls recommend_oss against real APIs: see scripts/smoke.mjs.
License
MIT
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Maintenance
Resources
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Looking for Admin?
If you are the server author, to access and configure the admin panel.
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