Entity SEO audit for schema, authors, and brand signals.

Run this report from the CLI, an MCP client, or application code. Every surface uses the same report definition and returns the same evidence. JSON is the source of truth; Markdown makes it readable without hiding dates, limits, warnings, or skipped work.

What this report helps you decide

What machine-readable and on-page entity signals were observed in this crawl?

• An organisation, person, or publisher needs a consistency review.
• You want missing or contradictory evidence separated from optional enhancements.

Command facts

Report id

entity-readiness

Execution

Local process

Outputs

JSON and Markdown

Example parameters

reportId

Agent discovery

seo reports describe entity-readiness --json

Interactive prompts

Human CLI commands only

When this report is not the right tool

These cases need a different report, more evidence, or a human decision. Do not force this report to answer a question its data cannot support.

• You need the broader crawl, index, snippet, page-structure, and optional resource checks used for AI search readiness. Recommended report: Check AI search technical readiness. Run AI readiness. It adds technical access and page-level readiness evidence beyond the identity and publisher signals reviewed here.
• You need proof that Google or an AI product has recognised a particular person, organisation, brand, or topic entity. No automated report in this package can observe a search engine's internal entity understanding. Review visible search results, knowledge features, primary profiles, authoritative references, and structured-data validation. This report can still show which names, authors, schema types, and sameAs links are present or inconsistent.

Data sources and inputs

• Saved or fresh crawl extraction. Provides page names, authors, publication dates, social links, and structured-data entities.
• Structured data and sameAs evidence. Provides observed organisation, person, article, profile, and external identity references.

What this report checks

• Reviews repeated site and publisher naming, author and date evidence, supported schema types, and sameAs links.
• Separates missing, inconsistent, invalid, and merely optional signals without producing an entity score.

How it works

• Collects observed signals, validates parseable structure, and reports consistency checks without an entity-recognition score.

The JSON result keeps dates, thresholds, limits, skipped work, and source completeness beside the finding. Missing, partial, capped, filtered, and complete data remain different states.

Run the report from the CLI

Review naming, authorship, schema, and sameAs evidence. Agents and CI should inspect the live schema before their first run.

seo entity-readiness --project example

seo reports describe entity-readiness --json

seo reports run entity-readiness --params '{"reportId":"crawl_example_20260710"}' --json

Project profiles can fill supported property and analytics inputs for the human-facing commands. The catalog form shown here is explicit by design, so agents and CI jobs do not prompt or guess.

How an MCP agent should use it

Call seo_describe_report first so the agent sees when this report is useful and gets the current input schema. Then callseo_run_report with the validated parameters. Read the status, warnings, source limits, and skipped sections before acting on a finding.

{
  "id": "entity-readiness"
}

{
  "id": "entity-readiness",
  "params": {
    "reportId": "crawl_example_20260710"
  }
}

Use a follow-up report returned by the result instead of guessing the next tool. The local MCP server and CLI use the same report definition and evidence. Their outer transport envelopes differ.

Use the report in a TypeScript app

Install seo as a project dependency, then call the same report catalog used by the CLI and MCP. executeReportrejects an unknown report id or invalid parameters. Provider and runtime failures come back as structured results withisError set.

npm install seo

import { executeReport } from 'seo/mcp'

const result = await executeReport(
  'entity-readiness',
  {
  "reportId": "crawl_example_20260710"
},
)

console.log(result)

The TypeScript library guide also covers direct core functions, schema discovery, and the difference betweenexecuteReport and runReport.

What comes back and how to read it

• Observed entity and publisher signals with representative pages, schema types, names, authors, dates, and sameAs links.
• Limited gaps and verification steps with explicit caveats about search-engine understanding and eligibility.
• Fix factual conflicts and invalid markup. Add optional fields only when they accurately describe visible content and the real entity.

Start with dataStatus, source details, warnings, and caveats. Then inspect the observed evidence before derived findings or suggested actions.

What this report cannot tell you

• Valid structured data does not guarantee recognition, rich results, rankings, or inclusion in an answer.

What to do next

1. Correct inconsistent source facts and rerun the crawl.
2. Use AI readiness for the broader technical access review.

Related reports

• Check AI search technical readiness. Review crawl access, indexability, snippet controls, page structure, and optional agent resources without inventing an AI visibility score.
• Audit one page. Inspect one live URL before changing its metadata, canonical, directives, structured data, links, or content.
• Crawl a site for technical SEO issues. Map a limited part of a site, run the maintained technical checks, and save a reusable evidence baseline.

Sources behind the guidance

These primary sources define the provider data or search controls used in the interpretation above.

• Google structured data guidance

Browse all reports in Crawling and technical checks.