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AIsa On-Page Audit

Audits in OnPage Lighthouse API

get_dataforseo_on_page_lighthouse_audits
Read-onlyIdempotent

The OnPage Lighthouse API is based on Google’s open-source Lighthouse project and provides data on the quality of web pages.

Usage-based billing; Default request estimate: $0.012 USD (not a fixed price). Account-specific pricing and final settlement may differ.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / x-fastmcp-wrap-result
      Added value: +true
  2. Changed1 schema field changed
    • removedOutput schema / x-fastmcp-wrap-result
      Removed value: -true
  3. Changed1 schema field changed
    • addedOutput schema / x-fastmcp-wrap-result
      Added value: +true
  4. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description adds genuinely new context by disclosing usage-based billing with a $0.012 default request estimate, which affects invocation decisions, but it says nothing about result latency, asynchronous task availability, or data freshness.

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?

Very short and front-loaded, with the purpose sentence first and the pricing note appended. The injected pricing block is boilerplate rather than tool-specific content, but it is compact and does not bury the point.

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 spelled out, and zero parameters keep the surface simple. However, for a billing-relevant DataForSEO endpoint sitting among ~25 siblings, the definition omits what an 'audit' result contains, when it is available, and how it relates to the submit/live endpoints, leaving real gaps for correct selection.

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?

The tool takes zero parameters, so per the rubric the baseline is 4. With an empty schema there is nothing for the description to disambiguate, though it also gives no indication of how the audits are scoped or retrieved without inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource (OnPage Lighthouse data on web page quality) but never states the actual action of this endpoint: retrieving stored/produced audits. It reads as a general blurb about the underlying API rather than a definition of this tool, and it draws no line against siblings such as post_dataforseo_on_page_lighthouse_live_json.

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?

There is no guidance on when to call this rather than the many sibling endpoints (submit, live_json, summary, etc.), no prerequisites, and no mention of which task IDs or site context would be needed to obtain results. Usage is entirely left to inference from the name.

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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