whichlib

The dependency picker for coding agents. Ask which library to use and get a scored, verified answer instead of a guess.

whichlib is an MCP server with three tools (recommend_repos, compare_repos, trending_repos) and a free dashboard, Fresh Repos, that shows the most-starred GitHub repositories created in the last day, week and month. Every repository gets a transparent 0–100 score from momentum, maintenance, adoption (stars, forks, npm and PyPI downloads) and license, plus a one-line verdict.

Quick start

Agents: see MCP server below for the one-line install.

Dashboard:

1. Clone or download this repository.

2. Double-click whichlib/dashboard/index.html.

That is all. The page is a single HTML file that calls the GitHub Search API straight from your browser. No build step, no server, no account.

Optional: paste a GitHub token under Settings on the page to raise the API limit from 10 to 30 requests per minute. A fine-grained token with no permissions is enough. It stays in your browser's local storage.

Related MCP server: mcp-github-tools

What you get

• Three tabs: Today, This week, This month. Each lists the 100 most-starred repos created in that window.

• Sort by any column: stars, stars per day, forks, open issues, created date, last push, language, license or name. Click again to reverse.

• Filter by language (17 languages) or by free text over name, description and topics.

• The rank column always shows the stars rank, so after sorting by forks you still see where a repo stands.

• Results are cached in the browser for 60 minutes per tab and language.

• Light and dark themes follow your system setting.

How the numbers are defined

• Trending here means "created in the period, ranked by stars". That is what the GitHub Search API supports. GitHub's own trending page ranks by stars gained in the period, which has no public API. The nightly snapshots in this repo will make that possible later.

• Downloads do not exist for repositories on GitHub, only for release files. Forks are shown as the nearest public signal.

• Stars/day is stars divided by the repo's age, floored at one hour.

Score

Every repo gets a score from 0 to 100, a tier and a one-line verdict. The breakdown is always returned so a person or an agent can see why. The same file, whichlib/lib/score.js, runs in the dashboard and in Node, so the two can never disagree.

Tiers: Strong 75 and above, Solid 50, Watch 25, Avoid below 25. The names are chosen to read correctly for a six-week-old project and a six-year-old library alike. Archived repos are capped at 20 and get the verdict "Archived, avoid." A missing license is always named in the verdict.

Verdicts read like "Rising fast, 10.6k downloads/wk, pushed 2 days ago, MIT", "Gaining steadily, 145M downloads/wk, quiet for 6 months, widely used, BSD-3-CLAUSE" or "Slow growth, no push in 60 days, GPL-3.0".

Downloads

GitHub has no download count for repositories, but package registries do. After each snapshot, the enrich step maps JavaScript and TypeScript repos to npm and Python repos to PyPI, then fetches last week's downloads:

• A package counts as the repo's only when the registry's own metadata links back to github.com/<owner>/<repo>. A matching name alone is never enough, so a new repo called widget is not credited with the downloads of an unrelated widget package.

• Candidates tried: <repo> and @<owner>/<repo> on npm, <repo> on PyPI.

• Mappings are cached in registry-map.json on the data branch. Negatives are re-checked after 7 days, positives kept, downloads refreshed daily.

• On the first run, 63 of 858 eligible repos mapped to a package. Most repos under a month old are not published yet, which is expected.

Other languages (Rust, Go, Java...) are skipped for now. Cargo, Go and Maven can follow the same pattern.

Caveat: opened from disk, the dashboard has no snapshot history, so momentum uses the fallback. Scores on the Today tab are therefore provisional; the report and the MCP server use real stars-gained figures once there are two or more days of snapshots.

cd whichlib
npm run score      # top 25 repos from the latest snapshot with score and verdict

MCP server

The same score, served to coding agents. Three tools over stdio:

Every result carries readable text and structuredContent (JSON) with the score, tier, verdict, the four subscores, flags, packages and downloads.

Install into Claude Code (replace the path with your clone; npx whichlib once it is published to npm):

claude mcp add whichlib -- node E:\private\whichlib\mcp\server.mjs

Cursor, Windsurf and others take the same command in their MCP config:

{ "mcpServers": { "whichlib": { "command": "node", "args": ["E:\\private\\whichlib\\mcp\\server.mjs"] } } }

Environment variables, both optional:

• GITHUB_TOKEN raises GitHub's limits (search 10 to 30 per minute). A fine-grained token with no permissions is enough. Recommend makes three searches per call, so without a token it allows about three recommendations per minute.

• FRESH_REPOS_DATA_DIR points at a folder of daily snapshots. The default is whichlib/data/snapshots, filled by npm run pull-data. With two or more days present, momentum uses real 7-day stars gained.

• WHICHLIB_TELEMETRY=off or DO_NOT_TRACK=1 disables anonymous call counting. What is counted: tool name, a random install id, version, platform and Node major version. Never queries, repository names or results. The collector is a small Cloudflare Worker in telemetry/, and its aggregate numbers are public at https://whichlib-telemetry.todorovskijosif.workers.dev/stats.

Try it without a client:

cd whichlib
npm run mcp:smoke   # starts the server over stdio, lists tools, calls each one

Known bias, reduced: maintenance used to drop to zero at 90 days without a push, which put httpx (145M weekly downloads, six quiet months) in "Watch". The curve now runs to a year and the stability guard keeps widely used repos at 0.5 or better; httpx lands in "Solid". Release cadence from the GitHub releases API is the proper long-term signal and is still to come.

Recommendation eval

mcp/eval/needs.json holds 20 needs ("pdf parser" in Python, "state management" in TypeScript, ...) each with a set of accepted answers a senior engineer would consider reasonable. npm run eval runs them through recommend_repos live and reports how often an accepted repo appears at rank 1, 3 and 5, for our ranking and for baselines built from the same candidate pool. Reports land in mcp/eval/results/.

Result on 2026-09-27, after query expansion (second report in results/):

The first report, before expansion, had the same hit rates for our ranking (75 / 95 / 100, MRR 0.86) on a smaller pool. Expansion raised recall from 53 to 74 accepted repos across the 20 pools, never fewer on any need, and the baselines fell on that noisier pool while ours held. The fit rules are what keep the noise out.

How recommend finds and ranks candidates

Retrieval, three GitHub searches per need:

1. Text search in GitHub's relevance order, with known synonyms OR-ed in (async OR asynchronous runtime), so vocabulary differences stop hiding libraries like tokio.

2. The same text search in stars order, for the big names whose description only mentions the subject.

3. One topic query sorted by stars (topic:cli, topic:image-processing), which surfaces what maintainers tagged themselves. The head word is used when it is specific (pdf, cli, orm) and the hyphenated phrase when it is broad (image-processing, state-management). GitHub rejects OR between topics, so it is one per request.

Language filters use families: JavaScript includes TypeScript and Python includes Jupyter, because many libraries moved to TypeScript.

Ranking key is fit = score × relevance:

• relevance is 1.0 at GitHub relevance rank 1 falling to 0.5 at rank 25, 0.75 when found only through the topic query, 0.4 when found only in the stars order;

• ×0.75 when the repo names the subject only in its topic tags and ×0.5 when nowhere in its name, description or topics (it matched README text only);

• ×0.8 when you asked for a framework, library, parser or client and the repo reads like an application rather than a building block.

Both score and fit are returned, with the relevance rank, the sources the repo came from and the two signals, so an agent can see why.

node mcp/eval/inspect.mjs "<need>" [language] [wanted/repo ...] prints the whole candidate pool for one need with these values.

Nightly snapshot job

whichlib/snapshot/ is a zero-dependency Node 22 script that stores the top 100 repos for 3 periods times 9 languages into whichlib/data/snapshots/YYYY-MM-DD.json. Consecutive snapshots are what a momentum score needs.

cd whichlib
npm test          # 16 unit tests, no network
npm run snapshot  # about 3 minutes without a token, 1 minute with GITHUB_TOKEN

A GitHub Actions workflow (.github/workflows/snapshot.yml) runs the job every day at 06:17 UTC and commits the result to the data branch, so history accumulates without bloating main. Trigger it by hand from the Actions tab or with gh workflow run snapshot. Bring the files down locally with:

cd whichlib
npm run pull-data  # copies new snapshots from origin/data into data/snapshots/
npm run score      # now with real 7-day stars gained once there are 2+ days

A Windows Task Scheduler alternative is in whichlib/README.md.

Repository layout

whichlib/             the npm package: MCP server, score, dashboard, jobs, eval, tests
whichlib/dashboard/   Fresh Repos, one HTML file
whichlib/lib/         score.js, shared by browser and Node
whichlib/mcp/         MCP server: recommend_repos, compare_repos, trending_repos
whichlib/snapshot/    snapshot job, enrichment, history builder, score report
whichlib/server.json  MCP registry manifest
telemetry/            call counter: Cloudflare Worker + Analytics Engine
RELEASING.md          release steps

Roadmap

1. Done: dashboard, nightly snapshots, transparent score with tiers and verdicts, npm and PyPI downloads, MCP server with recommend, compare and trending tools.

2. Named whichlib. Package shape, server.json for the MCP registry and the call counter are ready; remaining: deploy the counter, make the repository public, npm publish, mcp-publisher publish, list in the Claude Code plugin marketplace and the awesome-mcp lists.

3. Then wait four weeks and read weekly active installs and calls per install. That decides whether the team tier with policy rules gets built.

4. Later: Cargo, Go and Maven adoption; release cadence in maintenance; downloads in the dashboard; grow the eval past 20 needs from real usage.

License

MIT.