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 "Deploy 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-mcp recommend 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.
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).
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.
Raw GitHub repository search with normalized output.
Deep signals for one repository: star momentum, maintenance freshness, npm/PyPI adoption, HN buzz, and a 0-100 score.
Side-by-side trend comparison of 2-6 repositories, ranked by composite score.
Related MCP server: oss-intel-mcp
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
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.
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.
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.
MIT
Side-by-side trend comparison of 2-6 github repositories, ranked by composite score.
Inputs: repos (required).
Given a task or feature description, recommends trending open-source projects using GitHub star momentum, npm/PyPI download growth and Hacker News buzz. Pass focused github search terms in queries for best results.
Inputs: task (required), limit, queries, ecosystem.
Deep signals for one github repository: star momentum, maintenance freshness, npm/PyPI adoption, HN buzz, plus a transparent 0-100 score breakdown.
Inputs: repo (required).
Raw github repository search with normalized output. Use for ad-hoc exploration.
Inputs: query (required), sort, limit, minStars, +1 more.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 4 tools
Each tool serves a distinct purpose: recommendation, raw search, in-depth single-repo health, and multi-repo comparison. There is no overlap in functionality, so an agent can easily select the right tool for the task.
Three tools follow a verb_noun pattern (recommend_oss, search_repos, compare_repos) while repo_health is noun_noun. The style is uniform in lowercase with underscores, and the one deviation is minor and still intuitive.
With 4 tools, the server is tightly scoped to its purpose of OSS trend analysis. Each tool covers a distinct aspect without bloat, making the set manageable and well-focused.
The surface covers the core workflow: discover via search, get recommendations, deep-dive into one repo, and compare multiple. Missing features like historical trend charts or per-repo dependency analysis are minor gaps that don't block primary use cases.
Finds real, maintained open-source repos that fit your project. MCP grounding for coding agents.
Search, rank and compare open-source AI projects with Archestack's open data
Search agent-ready open source repos by task, interface, ecosystem and OG score.
GitHub project health, package dependency risk, trending repos, license & package comparison.
