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Start a Website SEO/AEO audit

start_audit

Create a stored SEO/AEO audit of a public business website without an account or data connection. Accepts optional dated provider reports. Reads the homepage, up to four internal pages, robots.txt, sitemap.xml and up to five relevant competitor websites discovered from public market research or supplied by the client. Samples three unbranded customer questions about the category, a task and a problem through live OpenAI API web searches when available. Newly audited competitors use the same questions and source-check rubric. Recent public research may be reused with its original dates; private client data is excluded from that cache. Reports include sourced public business contacts for manual review; the service does not draft or send outreach. Returns an audit ID for retrieving progress and the completed report. Reports include sourced findings, exact target URLs, up to three priorities, relevant content drafts, expected impact, realistic review points and measurement steps. The service cannot access other MCP connections, infer private metrics or edit websites.

Starts the work and returns an id. It usually takes several minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteYesThe client's public HTTPS website, such as https://lightsource.ai/.
goalsNoThe target customer and desired customer action, when known.
hostingNoWhere the client publishes the website, such as Framer, Webflow or WordPress.
shared_dataNoOptional snapshots collected with the client's permission through their own tools. Snapshots contain report data rather than credentials. The audit can run without them.
priority_urlsNoUp to three priority pages on the client's website. These replace the automatic page selection.
competitor_urlsNoOptional public competitor websites. Findable checks relevance to the same buyers and market before comparing them.
research_competitorsNoWhether the public audit includes relevant competitor discovery and comparisons. Defaults to true.
refresh_public_researchNoFetch fresh public evidence and analysis even when recent research exists, for example after website changes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior5/5

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

With readOnlyHint=false and openWorldHint=true already declared, the description goes well beyond them: it enumerates exactly what gets read (homepage, up to four internal pages, robots.txt, sitemap.xml, up to five competitors), discloses live OpenAI API web searches, cache-reuse rules with original dates, and that private client data is excluded. It also states the service will not draft or send outreach and returns an ID, which is unusually rich behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is one dense paragraph with no front-loaded summary or structure, and the trailing "Starts the work and returns an id. It usually takes several minutes." partly repeats the earlier audit-ID sentence. Most content is load-bearing given the absence of an output schema, but the wall-of-text form costs readability.

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 an 8-parameter tool with no output schema, the description covers what the audit does, what the report contains (sourced findings, target URLs, priorities, drafts, impact, review points, measurement steps), and the async return contract. It leaves out polling cadence and failure behavior, which an agent would still need.

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% and every parameter carries its own description, so the schema does the heavy lifting and a 3 is the baseline. The description reinforces semantics like competitor discovery and the no-connection constraint, but adds no format or value detail the schema lacks.

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

Purpose4/5

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

The opening sentence gives a specific verb and resource ("Create a stored SEO/AEO audit of a public business website") and qualifies the scope ("without an account or data connection"). The closing "Returns an audit ID for retrieving progress" makes the async pairing with check_audit inferable, but the sibling is never named outright, so it falls short of full sibling differentiation.

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

Usage Guidelines4/5

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

It states the operative context (no account or data connection required, optional provider snapshots, audit runs without them) and draws explicit boundaries ("cannot access other MCP connections, infer private metrics or edit websites"). It never explicitly says to poll with check_audit or when an agent should not start an audit, so exclusions are partial.

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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