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Lab test

lab_test

Starts a lab test of an audit's page: Lighthouse run by Google PageSpeed Insights (performance, accessibility, best practices, SEO). Answers 202 at once; the test takes ~25 s to 2+ minutes — then read it with lab_result. Does not change the audit score. Answers 202 at once; the test runs for ~25 s to over 2 minutes and its summary is stored in the audit (summary.lab.<strategy>), so it comes back in GET /api/audits/:id, in the tab and in the shared report. It does not change the audit score. Calling again while the same strategy is running does not start a second test. Limits: 4 tests at a time on the server, 6 per network per hour and 500 per day for the whole product.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesaudit id
strategyNomobile (default) or desktop
guest_tokenYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Far exceeds the annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false) by disclosing the 202 async contract, 25s–2min duration, that the score is unchanged, where the summary lands (summary.lab.<strategy>), dedup behavior per strategy, and concrete throttling limits (4 concurrent, 6/hour, 500/day). This is exactly the behavioral context an agent needs to use an async mutation safely.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded well with the core action first, but it is heavily redundant: 'Answers 202 at once', the timing range, and 'does not change the audit score' each appear twice verbatim, bloating a description that could be half its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the full load and does: it explains the 202 return, how to retrieve results, where they persist, timing, and rate limits. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is a moderate 67%; the description adds meaning beyond the schema by tying 'strategy' to the storage path summary.lab.<strategy> and to the dedup rule (same strategy won't double-run). It does not clarify guest_token, which the schema also leaves undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Starts a lab test of an audit's page') and immediately qualifies it as a Lighthouse run via Google PageSpeed Insights with the four measured categories. It is unambiguously distinguishable from its read counterpart lab_result and from audit_site/audit_url.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the agent to lab_result to read results, and gives a when-not condition ('calling again while the same strategy is running does not start a second test'). It does not, however, contrast itself against the other audit-running siblings (audit_site, audit_url, run_tab), so the 'why this tool vs those' choice is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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