convex-insights
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/convex/agent-skills --skill convex-insights- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads convex-insights automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
Query logs + health in natural language
The deployment already records what happened; the agent just has to ask well. This capability is a disciplined wrapper over the official Convex MCP's read tools (logs, insights, functionSpec, status) that turns operational questions into narrow, evidence-returning queries and hands back answers a human can one-click verify in the dashboard. The discipline is copied from the observability MCP surface that works best in the wild: discover fields before querying, three views not fifteen tools, token-frugal output, and a dashboard deep link on every answer.
Workflow
- GUARD: deploy-guard step 0-1 — identify + announce which deployment is being read. Reading logs/insights is read-only; never enable prod mutation flags for an insights pass.
- DISCOVER before you query — never guess identifiers. Use
functionSpecto list the real function names andstatusfor the deployment/version. Note the tool limits up front:logstakes only--history <n>(a COUNT, not a time window),--success,--jsonl,--prod,--deployment— there is NO server-side status/function/requestId/time filter;insightshas no function filter and is cloud dev/prod + user-auth only. So you fetch a recent window and filter CLIENT-SIDE. - PICK ONE OF THREE VIEWS and fetch the raw window, then filter locally:
- failures view →
logs --history <n> --jsonl, then locally keep failures + group by function + error message, returning counts + the first stack per group. Answers 'what's erroring', 'what failed after deploy'. - health view →
insights(cloud only): the typed 72h read-limit / OCC events. Surface + rank them, but hand perf/cost ROOT-CAUSING and fixes to convex-advisor — emit those as pointer findings, do not own the perf-fix framing here. - trace view →
logs --history <n> --jsonlthen locally filter to one requestId/function to read the full execution. Answers 'why did THIS call fail'.
- failures view →
- SCOPE by fetching a bounded recent window (a sensible
--historycount) and filtering client-side to the function/status/requestId asked about; when the window is large, aggregate (counts by function/message) rather than dumping lines. - ANSWER with (a) the one-line finding, (b) the evidence (counts + one representative stack/log line), and (c) WHEN POSSIBLE an agent-constructed dashboard deep link (dashboard.convex.dev, the deployment's Logs/Functions view) for human verification — no tool returns the link, so build it from the deployment name + function; never a raw log dump as the answer.
- CROSS-CHECK deploy causality when asked 'did my deploy break this': compare the failure onset (from the log timestamps) against the deployment version from
status; correlate, don't assert. - HAND OFF, don't fix here: a perf/cost cause → convex-advisor (which owns those fixes); a code defect → convex-reviewer/convex-authz; a live error to react to going forward → monitor/sentinel. Emit findings on the bus (specs/finding.schema.json) — primarily
observability, with perf/cost as pointer findings to advisor — so a composite pass can pick them up.
Rules
- Discover real function/field names (functionSpec/status) before filtering — never guess identifiers, never return a confusing empty result for a name the app doesn't have.
logsandinsightshave NO server-side status/function/requestId/time-window filter (logs takes only a --history COUNT; insights is cloud-only) — fetch a bounded recent window and filter CLIENT-SIDE; say so rather than implying params that don't exist.- One of three views per question (failures / health / trace) — don't fan out into many speculative tool calls.
- No tool returns a dashboard link — construct it from the deployment name + function when possible for human verification; never answer with a raw log dump.
- Read-only always: an insights pass runs no mutation and never enables prod mutation flags (deploy-guard discipline).
- Stay a reader and defer perf/cost fixes to convex-advisor: emit primarily
observability, route perf/cost as POINTER findings so advisor uniquely owns the perf-fix framing; forward-looking reaction goes to monitor/sentinel.
FAQ
What does the convex-insights skill do?
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
How do I install convex-insights?
Run npx skills add https://github.com/convex/agent-skills --skill convex-insights in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/convex-insights/ (personal) or .claude/skills/convex-insights/ (project).
Which AI agents can use convex-insights?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is convex-insights safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk flagged items to review. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains convex-insights?
It is published by convex in convex/agent-skills. It has 36,265 recorded installs.
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