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Run a WWW audit on one or more URLs

create_audit

Audit URLs for on-page signals that help AI assistants cite pages, including schema, headings, crawlability, and authority. Returns pending; read with get_audit until complete.

Instructions

Audits the given URLs for the on-page signals that help a page get cited by AI assistants: schema markup, breadcrumbs, heading structure, crawlability, authority signals, reading level and writing style.

Running one is instant; auditing takes under a minute. The audit comes back pending and turns success (or partial / error) once every URL has been checked — read it with get_audit until it does.

Give projectId to bill the audit to that project's workspace plan; leave it out to bill it to the API key holder's own plan. Either way the audited URLs count against that plan's monthly URL quota — get_audit_usage reads it first.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`urls`	Yes	The URLs to audit, each with its protocol.
`projectId`	No	Bill the audit to that project's workspace plan instead of the API key holder's own plan. The active project's id is what get_active_project reports. Left out, the key holder's own plan is used.

Output Schema

TableJSON Schema

Name	Required	Description
`id`	Yes
`status`	Yes	pending the moment it is requested; success once every URL succeeded, partial when only some did, error when none did.
`endDate`	Yes	When it finished, ISO 8601 in UTC. null = not finished yet.
`results`	Yes
`duration`	Yes	How long the audit took, in seconds.
`projectId`	Yes	The project this audit was billed to. null = run without one.
`startDate`	Yes	When the audit started, ISO 8601 in UTC.
`numberOfUrls`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.0.22Oct 2, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations mark it non-read-only but non-destructive; the description adds the crucial async lifecycle (pending → success/partial/error), timing expectations (instant to under a minute), and the quota/billing side effect of auditing URLs. These are real behavioral traits beyond the annotations.

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?

Three short paragraphs, front-loaded with purpose, then lifecycle, then billing. Every sentence carries information; the billing paragraph is slightly dense but justified given two plans and a quota.

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

Completeness5/5

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

With an output schema present, the description needn't explain return fields; it instead covers the asynchronous completion model, polling target, and billing/quota consequences. Nothing needed to invoke it correctly is missing.

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?

Schema coverage is 100%, so the baseline is 3. The description still adds meaning the schema does not: that audited URLs count against the selected plan's monthly URL quota, and that omission falls back to the key holder's plan. It reinforces rather than merely restates projectId.

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 (audits) and resource (given URLs) and enumerates the concrete signals checked: schema markup, breadcrumbs, headings, crawlability, authority, reading level and style. This clearly distinguishes it from sibling read tools like get_audit, which only fetches results.

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

Usage Guidelines5/5

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

Explicitly describes the workflow: run it, it returns `pending`, poll with get_audit until success/partial/error, and check quota with get_audit_usage. It also tells the agent how to choose billing via projectId vs omitting it, which is genuine when-to-use guidance.

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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