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xcalibur73

WebAudits: Performance & GEO Diagnostics

by xcalibur73

Audit DOM Bloat & Tree Depth

audit_dom_bloat
Read-onlyIdempotent

Analyze a public URL's HTML tree to report DOM node count, depth, div ratio, and Lighthouse violations, then recommend component chunking and shallow nesting fixes.

Instructions

Analyzes HTML tree structure of a public URL to report total DOM nodes, maximum depth, div ratio, and Lighthouse threshold violations. Recommends component chunking and shallow nesting fixes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe target website URL including protocol (e.g. https://example.com).
response_formatNoOutput format: 'markdown' for human-readable diagnostic or 'json' for machine processing.markdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds the analysis scope and what it recommends, but says nothing about fetch behavior, timeouts, or how the URL is retrieved, leaving meaningful behavioral gaps.

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?

Two tight sentences, front-loaded with verb+resource, followed by the concrete metrics reported and the remediation suggestions. No wasted words and every clause carries information.

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?

With no output schema, the description carries the return-value burden and does so by naming the four metrics and the recommendation content. It's nearly complete for a read-only audit, though it omits any note on how or when the URL is fetched.

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?

Schema description coverage is 100%, so both url and response_format are fully documented in the schema, and the enum makes the format choice self-explanatory. The description adds no format syntax or semantic detail beyond the schema baseline.

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?

States a specific verb (Analyzes) and resource (HTML tree structure) and enumerates concrete outputs (DOM node count, maximum depth, div ratio, Lighthouse threshold violations). This is specific enough to naturally distinguish it from audit_geo_readiness and audit_lcp_trace without needing to name them.

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?

No when-to-use guidance, no conditions for choosing this over the sibling audit tools, and no exclusions or prerequisites. The only context clue is 'public URL,' which hints at the input requirement but not usage.

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