Skip to main content
Glama

audit_consistency

Read-only

Audits multiple pages for consistent container width and hero heading tier. Detects outliers and scores consistency to flag cross-page issues.

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.
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is known. The description adds valuable context: inference of canonical values from corpus, pure offline execution, and detailed output structure. 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.

Conciseness5/5

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

The description is front-loaded with purpose, then provides usage instructions, behavioral notes, and output details. Every sentence adds value, and no redundant information is present.

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 no output schema, the description fully describes the return values (per-page extraction, consistency dimensions, outlier pages, issues, score, summary). It covers all aspects needed for an agent to understand and use the tool correctly.

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 100%, meeting baseline. The description adds meaning beyond schema by explaining the purpose of each parameter in context, e.g., 'When supplied, hero divergence is measured against this token rather than the corpus modal.'

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 audits multiple pages for cross-page consistency of container width and hero heading tier, using specific verbs and resources. It distinguishes itself from siblings like audit_page by focusing on cross-page consistency rather than single-page evaluation.

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?

Explicitly says to pass ≥2 pages from different routes on the same site, and flags the blind spot where pages pass audit_page but disagree. This provides clear when-to-use and when-not-to-use guidance, and references the alternative audit_page.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/rhinocap/raven-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server