Real output, not a mockup. Your agent gets the same answers over MCP.
Why your agent needs this
A model's knowledge of npm stops at its training cutoff. The registry doesn't.
- Invented and typosquatted names. Agents confidently install packages that don't exist, or that
exist only because someone registered the misspelling.
- APIs that moved. Of the 38,752 packages lurq tracks, 2,725 shipped a new major version between
May 1 and September 13, 2026. Code written from memory calls functions that were renamed or removed.
- Stacks that don't install together. Every package can be healthy on its own while the set
fails on a peer or engine range. That usually surfaces at deploy.
lurq answers from evidence it can show: 47,238 packages scored, 4.2M versions tracked, 34,843 API
surfaces extracted from shipped code, and 27M co-install pairs, refreshed daily from npm, GitHub,
deps.dev and OSV. Every answer carries a dataAsOf timestamp.
Scope: the JavaScript/TypeScript ecosystem (npm).
Install
One command, once per machine. Nothing to install first:
The setup wizard:
- Opens lurq in your browser. Sign in and the key comes back to the terminal by itself.
- Detects every coding agent on the machine.
- Writes a keyed remote MCP entry for each, in the shape that agent expects.
- Installs standing instructions where the agent supports them, so it reaches for lurq instead of
answering from memory.
Restart your agent afterward. The free plan includes 1,000 hosted calls a month and never asks
for a card.
Works with: Claude Code · Cursor · Windsurf · VS Code / GitHub Copilot · OpenAI Codex CLI ·
Gemini CLI · Google Antigravity · Kiro
Check it worked: ask your agent to "add zod to this project". It should call lurq's verify
before installing anything.
Claude Code plugin, no terminal needed:
One-click links for Cursor and VS Code are in the
quickstart.
The package is published as lurqrun (the bare name was taken). The command is lurq:
For AI agents
If you are an AI coding agent and your user asked you to set up lurq, follow these steps.
1. Run setup.
In your shell it does not prompt: it prints a one-time sign-in link and exits right away. Give the
user that link. When they sign in on this computer, lurq stores a key and connects every detected
coding agent by itself. Never invent a key.
2. Or configure non-interactively when the user already has a key from
lurq.run/dashboard/keys, or on a cloud, SSH or CI machine, where
a sign-in link on this computer can't work:
--agent all configures every detected agent. With --yes, the key can also come from LURQ_API_KEY.
Some checks need no key at all. npx lurqrun check-upgrade --upgrade <package>@<from>..<to> compares an
upgrade against the names your code actually imports, locally.
3. Tell the user to restart the agent, then confirm lurq appears in its MCP tool list.
Writing the MCP config by hand
Endpoint: https://api.lurq.run/mcp (streamable HTTP), header Authorization: Bearer <key>. The
field names differ by client:
Claude Code (~/.claude.json, under mcpServers) and VS Code (mcp.json, under servers):
Cursor (~/.cursor/mcp.json) and Kiro (~/.kiro/settings/mcp.json), under mcpServers:
Windsurf (~/.codeium/windsurf/mcp_config.json) and Antigravity (~/.gemini/config/mcp_config.json), under mcpServers:
Gemini CLI (~/.gemini/settings.json, under mcpServers). It must be httpUrl, because a
plain url is read as SSE:
Codex (~/.codex/config.toml). Headers go in an inline table:
When to call lurq
When lurq flags something, tell the user what it found and that it came from lurq. An unknown or
UNVERIFIABLE result means lurq could not check. It never means the package is clean.
Machine-readable docs: lurq.run/llms.txt ·
lurq.run/docs/llms-full.txt
MCP tools
Fifteen tools. Responses are compact to save tokens, and package answers carry dataAsOf.
Before installing
Across a stack
Writing code
MCP servers
About lurq
Inputs, outputs and verdict definitions for each: MCP tools reference.
CLI
The same index, scriptable. Every read command takes --json.
Full reference: CLI docs.
Autopilot
lurq keeps a repository's dependencies current, and names the code an upgrade breaks before it merges.
The check reads the symbol-level API diff between versions and matches it against what your code
actually references. It needs no tests.
Three modes, and editing is opt-in. The workflow starts in comment: it plans, checks, writes the
brief to the run summary, and changes nothing. fix opens a pull request containing only what the
package itself proves — renamed call sites, and the range bump in every manifest — so it needs no
Anthropic credential, and it is what a newly armed repo gets. pr is fix plus the agent, for the
changes a rule cannot make. Step 4 above is the only one that needs a model, so only pr does.
The dashboard setting governs repos that already installed the workflow: each run reads the mode from
the plan response rather than from the committed file.
Cadence follows scope — weekly for the scopes that track breakage, since majors arrive slowly enough
that a daily run mostly reports nothing new; daily for a repo set to advisories-only, because weekly
can mean seven days sitting on a known CVE.
Trust model. lurq's GitHub App holds Contents: read-only and Actions: write. It can read your
manifests and start the workflow you committed — it cannot write a byte to your repository, change
this file, or set a repository variable. Every write is made by the workflow itself using your own
ephemeral GITHUB_TOKEN, bounded by the permissions: block in the file you control. The agent's allowlist is
Read,Edit,Write,Bash(<pkg-manager>:*), so it edits files but never touches version control.
Removing it is git rm .github/workflows/lurq-upgrade.yml.
What leaves your machine
Package names and versions, never your source code. audit receives the inventory your agent reads
locally, check-upgrade matches symbols against your code on your own runner, and outcomes report
names and counts only.
lurq mcp-scan is the one exception worth knowing: by default it records what your MCP servers
expose (tool names, descriptions and parameters, with secrets redacted) to your account, so it can
tell you what changed. --no-upload keeps the scan on your machine.
Where the evidence comes from
Readable: npm, GitHub, deps.dev and OSV, re-synced daily. Downloads, release cadence,
maintenance, advisories, deprecations, license and bundle cost.
Executed: an isolated sandbox installs and imports package versions and records the results in a
compatibility matrix. compat reads those edges alongside declared peer and engine ranges, which is
why lurq can say whether a whole stack holds together.
Ranking is deterministic and public. No model sits in the scoring path:
Every weight lives in src/scoring/weights.ts and prints with lurq weights.
Plans
Past the limit, calls slow to 20 a day rather than stopping. The CLI run against your own database
is never metered. Details: pricing.
Uninstall, reinstall, and where setup writes
Setup is safe to re-run: npx lurqrun again overwrites the previous entries.
It lists what it will remove and asks first (--yes skips the question).
lurq logout only clears the CLI's stored key. A copy stays in each MCP entry until lurq uninstall
removes it, and the key keeps working until you revoke it in
the dashboard.
Self-hosting
lurq runs against your own Postgres too: lurq serve (stdio MCP) or lurq serve-http (a
rate-limited HTTP service), with DATABASE_URL set. The published CLI installs only what hosted use
needs; the first self-hosting command prints the exact npm install line for the server packages.
Guide: self-hosting.
Contact
Inquiries, partnerships or proposals: [email protected]
License
MIT