AI search readiness report for crawl access and citation 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

Does the crawl show technical restrictions or missing evidence worth reviewing for AI search?

• You need a technical readiness review grounded in a crawl.
• You want access and content observations kept separate from selection claims.

Command facts

Report id

ai-readiness

Execution

Local process

Outputs

JSON and Markdown

Example parameters

reportId

Agent discovery

seo reports describe ai-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 only need to check the Google crawl, index, canonical, and snippet controls used for AI feature eligibility. Recommended report: Check Google AI search controls. Run Google AI search controls. It narrows the evidence to Google-supported technical eligibility controls and leaves optional page observations outside the blocker list.
• You need to know whether an AI product will cite, mention, rank, or send traffic to a page. No automated report in this package can decide that. Check the relevant AI products with a repeatable external monitoring method and review their returned answers and citations. This report can still identify technical controls that may prevent eligibility.

Data sources and inputs

• Saved or fresh crawl report. Provides response, robots, indexability, snippet, page structure, and optional resource evidence.
• Current Google AI feature guidance. Defines which normal crawl, index, and snippet controls also apply to Google AI search features.

What this report checks

• Reviews fetch access, robots rules, indexability, canonicals, and page-level snippet restrictions.
• Reports page structure, structured data, and optional agent resources as observations rather than citation or visibility requirements.

How it works

• Evaluates each evidence group independently and returns unknown when the crawl cannot support a pass or failure.

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 AI search access and page signals from the latest crawl. Agents and CI should inspect the live schema before their first run.

seo ai-readiness --project example

seo reports describe ai-readiness --json

seo reports run ai-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": "ai-readiness"
}

{
  "id": "ai-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(
  'ai-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

• Technical eligibility blockers and affected pages grouped by the observed control.
• Separate optional observations, source status, crawl limits, warnings, and caveats with no aggregate score.
• Prioritise hard conflicts with publisher intent. Treat semantic structure and optional files as observations, not Google requirements.

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

• Technical eligibility never guarantees indexing, selection, citation, visibility, or traffic.

What to do next

1. Use geo gaps for Google-specific access and snippet controls.
2. Use AI referrals for observed referral sessions.

Related reports

• Check Google AI search controls. Find crawl, indexability, and snippet controls that can block Google AI search eligibility on selected pages.
• Check entity and publisher signals. Review naming, authorship, dates, schema, and sameAs links without claiming a search engine has recognised an entity.
• Find AI referral traffic. See which known AI products sent referral sessions recorded by GA4 and which landing pages received them.

Sources behind the guidance

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

• Google guidance for AI features and websites
• Google robots.txt guidance
• Google robots meta and snippet controls

Browse all reports in Crawling and technical checks.