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A1-x-Tech

Google CrUX MCP

CrUX weekly timeseries (raw)

query_history_record
Read-onlyIdempotent

Fetch weekly CrUX time series for an origin or URL, with up to 40 rolling 28-day windows. Returns raw histogram, percentile, and fraction data per metric.

Instructions

Returns the weekly CrUX timeseries for an origin or URL (raw API response, updated Mondays ~04:00 UTC): up to 40 collection periods, each a 28-day rolling window. Per metric: histogramTimeseries (bins with densities arrays), percentilesTimeseries.p75s and fractionTimeseries; all series align with record.collectionPeriods. Ineligible periods appear as null p75s and "NaN" densities — tolerate non-numeric entries. A no-data answer (HTTP 404) is returned as {no_data: true}. Prefer get_cwv_trend for a cleaned p75 trend.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoA specific page URL, e.g. https://example.com/pricing/. Mutually exclusive with `origin`. Pass the final post-redirect URL (the API does not follow redirects); fragments and query params are stripped by the dataset. Single pages have fewer samples and often have no data — fall back to `origin` on a no_data result.
originNoSite origin — scheme + host only, e.g. https://example.com (no path, no trailing slash). Aggregates real-user data across ALL pages of the site. Mutually exclusive with `url`. http/https and www/non-www are distinct keys; use the canonical variant.
metricsNoMetric names to return; omit for all available metrics. Timings are integer milliseconds; cumulative_layout_shift is a string-encoded double. form_factors is only returned when form_factor is NOT set.
form_factorNoDevice class filter. Omit for the aggregated record across all devices. tablet traffic is tiny and usually has no data.
collection_period_countNoHow many weekly collection periods to return (1..40; API default 25).
Behavior5/5

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

Annotations already declare read-only/idempotent behaviors. The description adds substantial context beyond annotations: update schedule (Mondays ~04:00 UTC), 28-day rolling windows, data structure (histogramTimeseries, percentilesTimeseries), null/NaN handling, and 404→{no_data:true}. No contradictions.

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

Conciseness5/5

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

Three dense sentences front-load the purpose. Every clause adds value: raw format, cadence, period count, data shape, edge cases, and sibling pointer. No filler or redundancy.

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?

This is a complex raw-timeseries tool with 5 parameters and no output schema. The description covers output structure, series alignment, non-numeric values, no-data response, and alternative tool, making it complete enough for correct selection and invocation.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline 3 applies. The description mentions 'origin or URL' and 'collection periods' but does not add parameter-specific semantics beyond the schema's already thorough descriptions.

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?

The description uses a specific verb and resource: 'Returns the weekly CrUX timeseries for an origin or URL'. It clearly identifies the raw API nature and distinguishes from siblings by explicitly recommending get_cwv_trend for a cleaned p75 trend.

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

Usage Guidelines5/5

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

An explicit alternative is provided: 'Prefer get_cwv_trend for a cleaned p75 trend', implying this tool for raw timeseries. It also gives practical usage caveats like no-data fallback to origin and update cadence, clarifying when to use.

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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