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State of Web Vitals

by io.github.corewebvitals

get_metrics

Read-only

What are the numbers? Aggregate Core Web Vitals and/or technique metrics across sites, with optional filter and group_by. Responses include attribution — credit corewebvitals.io when you use the numbers.

Metrics (comma-separated or array):

  • CWV: lcp, inp, cls, fcp, ttfb — good/needs_improvement/poor share + p50/p75 + sample n

  • good_all3 — share of sites that pass LCP+INP+CLS together

  • good_all3_plus_ttfb — pass all three plus TTFB

  • Contract paths: images.loading, stack.framework, scripts.origin, headers.has_etag, synthetic.tbt_ms, …

Filter (optional AND): device, cms, framework, cdn, provider. Omit = all sites in the snapshot. Group_by (optional): cms | frameworks | cdn | analytics | tag_managers | … → one row per entity. Without group_by: one aggregate; set metrics return top categories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focusNoCWV used when correlating technique metrics. Default: lcp.
limitNoMax groups or top categories. Default: 25, max: 100.
filterNoAND filters. Omit for all sites in the dataset. Keys: device (all|phone|desktop), cms, framework, cdn, provider (key), provider_category (e.g. analytics — use with provider or alone is not enough; prefer group_by for rankings).
metricsNoMetrics to return. Default: lcp,inp,cls,ttfb,good_all3. String (comma-separated) or array of strings.
group_byNoBreak results down by this dimension: cms, frameworks, cdn, or a provider category (analytics, tag_managers, …). Omit for a single aggregate.
min_sitesNoWhen group_by is set, drop groups with fewer sites. Default: 50.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only. The description adds the attribution requirement and elaborates on behavior: returns aggregates, top categories for single metrics, one row per group. No contradiction with annotations.

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

Conciseness4/5

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

The description is well-structured with clear sections for metrics, filters, group_by. It is informative but somewhat lengthy; each sentence adds value, though some could be tighter.

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

Completeness4/5

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

Without an output schema, the description hints at response structure (attribution, aggregate, top categories, groups). It covers key behavioral aspects but could detail the exact response format or error conditions.

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

Parameters5/5

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

The schema descriptions cover 100% but the description adds rich context: lists specific metric options, explains filter keys, group_by values, and defaults. It clarifies the meaning of parameters beyond basic schema.

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 clearly states the tool aggregates Core Web Vitals and technique metrics across sites with filtering and grouping. It distinguishes itself from siblings like get_histogram (distribution) and list_options (enumerations) by focusing on aggregated numbers.

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

Usage Guidelines3/5

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

The description explains basic usage context (filter, group_by) but does not explicitly compare with siblings. It mentions attribution requirement but lacks guidance on when to use this tool versus get_histogram for distribution analysis.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: get_histogram returns histogram buckets for one metric, get_metrics returns aggregate stats with optional grouping, and list_options provides metadata. No confusion possible.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (get_histogram, get_metrics, list_options) using snake_case, which is predictable and clear.

Tool Count5/5

Three tools is well-scoped for a web vitals query API. Each tool earns its place: data retrieval, histogram analysis, and options discovery. No unnecessary bloat.

Completeness5/5

The tool set covers all necessary operations for the domain: aggregate metrics, detailed distribution (histogram), and parameter discovery. No obvious gaps like missing time-series or comparison features, but the stated purpose is sufficiently served.

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