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audit_urls

Crawl a list of URLs to identify technical SEO issues: broken status codes, redirect chains, missing or duplicate meta tags, H1 problems, canonical issues, noindex, and thin content. Returns a per-URL breakdown and summary.

Instructions

Crawl a list of URLs and report per-page technical SEO issues: broken status codes, redirect chains, missing or over-length titles and meta descriptions, missing or duplicate H1, missing canonical, noindex, and thin content. Returns a per-URL breakdown plus an issue summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYes
concurrencyNo
timeout_secondsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It usefully states that the tool crawls URLs and returns a per-URL breakdown plus an issue summary, but it does not disclose operational behaviors such as crawl duration, rate limiting, redirect-following details, or auth/network requirements. This is adequate but not rich.

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 description is a single information-dense sentence that front-loads the action and resource, then lists issue categories and the return shape. It avoids repetition and wastes no words, though the enumeration makes it slightly dense.

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 the description does not need to detail return values, and it does give a useful high-level summary. However, with zero annotations, zero schema descriptions, and no usage guidance, the description leaves concurrency/timeout semantics and tool-selection boundaries undocumented, making it only moderately complete.

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%, so the description must compensate, but it never names or explains urls, concurrency, or timeout_seconds. The parameter names are somewhat self-explanatory, yet the description adds no detail about what concurrency or timeout_seconds control, how URLs should be formatted, or whether limits apply.

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 uses a specific verb-resource pair ('Crawl a list of URLs and report per-page technical SEO issues') and then enumerates the exact issue categories. This clearly distinguishes it from siblings like fetch_sitemap and sitemap_coverage by centering on per-URL technical SEO auditing, even though it overlaps with check_redirects on redirect chains.

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

Usage Guidelines3/5

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

The description implies its use case by listing SEO checks, but it never explicitly states when to prefer this over check_redirects, fetch_sitemap, or sitemap_coverage, nor does it give any 'when not to use' guidance. An agent can infer the purpose but must decide on selection criteria without direct help.

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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