State of Web Vitals
Server Details
Core Web Vitals metrics by CMS, CDN, and framework — free remote MCP, no auth.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.4/5 across 3 of 3 tools scored.
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.
All tool names follow a consistent verb_noun pattern (get_histogram, get_metrics, list_options) using snake_case, which is predictable and clear.
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.
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.
Available Tools
3 toolsget_histogramARead-onlyInspect
What is the shape of one metric? Returns histogram buckets (count + share) plus summary stats.
Metric: one of lcp, inp, cls, fcp, ttfb (field CWV), or a scalar technique path when available.
Filter: same as get_metrics (device, cms, framework, cdn, provider). No group_by — call get_metrics with group_by to compare segments, then get_histogram per segment if needed.
| Name | Required | Description | Default |
|---|---|---|---|
| filter | No | AND 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). | |
| metric | Yes | lcp | inp | cls | fcp | ttfb, or a contract scalar path. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and destructiveHint. Description adds return details (buckets, summary stats) but no additional behavioral traits like rate limits or permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short, front-loaded paragraphs with zero waste: purpose, metric list, usage guidelines.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 params and no output schema, description explains return type (histogram buckets + summary stats) and filter similarity. Could detail output structure more, but adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds value by listing valid metric values and summarizing filter structure as same as get_metrics, going beyond raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns histogram buckets and summary stats for a single metric, specifying valid metrics (lcp, inp, cls, fcp, ttfb) and contrasting with sibling get_metrics by noting no group_by support.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to use get_metrics with group_by for comparisons, then get_histogram per segment, and notes filter is same as get_metrics, providing clear when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_metricsARead-onlyInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | CWV used when correlating technique metrics. Default: lcp. | |
| limit | No | Max groups or top categories. Default: 25, max: 100. | |
| filter | No | AND 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). | |
| metrics | No | Metrics to return. Default: lcp,inp,cls,ttfb,good_all3. String (comma-separated) or array of strings. | |
| group_by | No | Break results down by this dimension: cms, frameworks, cdn, or a provider category (analytics, tag_managers, …). Omit for a single aggregate. | |
| min_sites | No | When group_by is set, drop groups with fewer sites. Default: 50. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark read-only. Description adds behavioral details: attribution requirement, return of shares/percentiles/sample n, and top categories behavior. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with bullet points and logical flow. Slightly verbose but every sentence adds value. Efficiently front-loads purpose and key usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description adequately explains return content (shares, percentiles, sample n) and top categories. Covers all parameters and usage modes. Could benefit from explicit response structure example.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%. Description adds value by listing available metrics (e.g., good_all3, contract paths) and explaining filter/group_by behavior beyond the schema. Baseline 3, boosted for additional context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 optional filtering and grouping. It distinguishes itself from siblings (get_histogram, list_options) by focusing on aggregate metrics with group_by and filter capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides guidance on when to use group_by vs aggregate ('Without group_by: one aggregate'), default behaviors ('Omit = all sites'), and mentions attribution. Does not explicitly compare to siblings but context suggests distinct purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_optionsARead-onlyInspect
Discover what you can pass to get_metrics / get_histogram: filter keys, group_by values, built-in metrics, and contract metric paths (optional kind filter). Call this when unsure of a key or path.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | Optional contract kind: scalar, boolean, categorical, set, weighted_set, measures. | |
| include_paths | No | Include full contract path list. Default true. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description reinforces this by characterizing the tool as discovery-oriented, adding context about its informational role.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. First sentence lists what is discovered, second tells when to use. Ideal structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple discovery tool with no output schema, the description fully covers purpose, usage context, and parameter roles. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers parameters completely (100%), but description adds context by linking parameters to the types of options discovered (filter keys, group_by values, etc.), beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it discovers valid parameters for get_metrics and get_histogram, and distinguishes itself as a lookup tool from the sibling tools that consume those parameters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Call this when unsure of a key or path,' providing clear guidance. Does not mention when not to use, but context makes it obvious.
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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{
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