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Glama

heading_audit

Audit a page's H1-H6 structure to ensure one H1, proper hierarchy, no title/H1 duplication, and meaningful section titles. Reports issues and words per H2.

Instructions

Audit the heading structure of a page: H1 uniqueness, hierarchy, and title overlap.

Checks that exactly one H1 exists, that heading levels do not skip (H2 straight to H4), that the is not a word-for-word copy of the H1 (a duplicate wastes a second angle on the target keyword), and that headings actually say something rather than filling a slot ("Introduction", "Conclusion").

Also reports words per H2, since a long page with no subheadings reads as a wall.

No Google API calls. No authentication required. Verdicts: healthy | issues_found | fetch_error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.2.0

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden well: it discloses that no Google API calls are made, no authentication is required, and that the tool returns one of three verdicts (healthy, issues_found, fetch_error), the last implying it may attempt a fetch. It does not state rate limits or explicitly confirm read-only behavior, but the operational context is strong.

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 core purpose is front-loaded, followed by specific checks and then brief operational notes. Some rationale (e.g., 'a duplicate wastes a second angle on the target keyword') is verbose but earns its place by explaining why the check matters.

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

Completeness3/5

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

An output schema exists, so return values need not be detailed in the description, and the tool's complexity is low (one required parameter). However, the absence of any parameter description and the lack of explicit read-only confirmation leave gaps that an agent would need to infer.

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

Parameters2/5

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

Schema description coverage is 0% for the sole 'url' parameter, and the description never specifies its format, whether it must be absolute, or how it is used. The phrase 'of a page' implies a page URL but adds no concrete meaning beyond the parameter name.

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?

Specific verb 'audit' and resource 'heading structure of a page', followed by an enumeration of exact checks: H1 uniqueness, level skipping, title-H1 overlap, meaningful heading text, and words per H2. This clearly distinguishes it from generic siblings like content_quality or page_technical_audit.

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

Usage Guidelines2/5

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

The description explains what the tool checks but offers no guidance on when to use it versus alternatives such as content_quality or page_technical_audit. It mentions no prerequisites beyond 'No authentication required', and gives no when-not or exclusion conditions.

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