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audit_consistency

Read-onlyIdempotent

Audit multiple pages to detect silent cross-page inconsistencies in container width and hero heading tier, catching issues that per-page audits miss.

Instructions

Audit multiple pages for cross-page consistency of content-container width and hero heading tier. Pass ≥2 pages ({name, html}) collected from different routes on the same site. Infers the canonical (modal) value from the corpus when no token is supplied, so you need not know the project's design token in advance. Flags the issue #9 single-blob blind spot: pages that each pass audit_page but silently disagree with each other on container width or hero size class. Returns per-page extraction (container_px, container_classes, hero_classes, signatures), consistency dimensions with reference values, outlier page names, issues[], score (100/50/0 → A/C/D), and a plain-text summary. Pure offline — no browser, no network.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pagesYesAt least 2 pages to compare. Each entry is {name, html}.
hero_tokenNoCanonical hero heading class signature (e.g. "text-display-xl" or "64"). When supplied, hero divergence is measured against this token rather than the corpus modal.
container_tokenNoProject's canonical container width in px (e.g. 1152). When supplied, container divergence is measured against this token rather than the corpus modal.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv2.2.9

TDQS

A4.5/5.0
Behavior5/5

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

The description goes well beyond the readOnlyHint and idempotentHint annotations by disclosing that the tool is pure offline with no browser or network, that it infers the canonical modal value when no token is supplied, and that it flags silent cross-page disagreements. This gives meaningful context about how the audit behaves, not just what it returns.

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?

The description is compact and well-organized: purpose, input contract, behavioral notes, blind-spot warning, then return summary. Every sentence contributes unique value, and the most important facts are front-loaded.

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?

Despite having no output schema, the description clearly enumerates what the tool returns: per-page extraction, consistency dimensions, outliers, issues, score, and summary. It gives an agent enough context to confidently invoke the tool and interpret the result without further lookups.

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?

The schema already describes all parameters, including the semantic difference between supplied tokens and corpus-modal inference. The description adds useful guidance about collecting pages from different routes and not needing the design token in advance, but it doesn't significantly expand on the individual parameter formats beyond the 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 states a specific verb and resource: audit multiple pages for cross-page consistency of container width and hero heading tier. It clearly differentiates itself from audit_page by naming the issue #9 single-blob blind spot where pages each pass individually but disagree with each other.

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?

The description tells the agent to pass ≥2 pages collected from different routes on the same site, making the primary use case explicit. It references audit_page as the related single-page tool, but it does not fully spell out all when-not conditions or explicitly compare against the wider set of audit siblings.

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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