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paulet4a-commits

WebDataTools Domain & website intelligence MCP server

seo_page_audit

Audit on-page SEO for any list of URLs: check titles, meta descriptions, headings, images, links, Open Graph, Twitter Cards, and Schema.org, then return one scored row per page.

Instructions

On-Page SEO Audit checks title, meta description, headings, images, links, Open Graph, Twitter Card and Schema.org data for any list of URLs — one scored row per page. Billed to your own Apify account: ~$0.002 per result (Apify free-plan price, lower on paid plans).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesURLs — Enter the page URLs to audit, one row is returned per URL, e.g. https://apify.com. A bare domain like apify.com is accepted and gets https:// added automatically. Example: ["https://apify.com"].
checkLinksNoCheck internal links for broken ones — Keep this off for a fast audit. Turn it on to HEAD-check up to 50 internal links per page and list the broken ones in the `brokenLinks` field — this is slower and adds extra requests per page.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full disclosure burden, and it does add real value: per-result billing (~$0.002, Apify account) and the cost/speed tradeoff of checkLinks (extra HEAD requests, up to 50 links per page). It still omits auth requirements, failure behavior, and rate limits, so the safety/operational picture is incomplete.

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 with zero padding: capability and output shape first, billing second. Every clause earns its place and the most decision-relevant information is front-loaded.

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?

For a 2-parameter, no-output-schema tool, the description covers capability, output granularity, cost, and the checkLinks tradeoff — enough for an agent to call it correctly. Only minor gaps remain, such as what a failure or empty result looks like.

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 parameters are fully documented in the schema itself (URL normalization rules, checkLinks semantics). The description adds no syntax or format detail beyond that, which is the expected baseline when the schema does the heavy lifting.

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?

Names a specific verb+resource (On-Page SEO Audit) and enumerates exactly what it inspects — title, meta, headings, images, links, OG, Twitter Card, Schema.org — with the output shape stated as 'one scored row per page'. It is clearly distinguishable from siblings like core_web_vitals_audit and tech_stack_detector.

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 when the tool fits (on-page SEO element auditing for a list of URLs) and notes that checkLinks should stay off for a fast audit, but it never states when to choose it over siblings or any exclusion conditions. Usage is inferred rather than directed.

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