AI search scorecard for crawl-based readiness scoring.

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

How do this tool's own AI-search technical checks summarise into a single scored read of one crawl?

• You want a compact scored summary of the AI-search evidence the crawler already collects.
• You need per-check pass, warn, fail, or unknown states with the exact weights and formula.

Command facts

Report id

ai-search-scorecard

Execution

Local process

Outputs

JSON and Markdown

Example parameters

reportId

Agent discovery

seo reports describe ai-search-scorecard --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 want the underlying access, indexability, and snippet evidence rather than a single number. Recommended report: Check AI search technical readiness. Run AI search readiness. It returns the evidence-only assessment this scorecard summarises, with no aggregate score.
• You need Google-specific crawl, index, canonical, and snippet controls for AI feature eligibility. Recommended report: Check Google AI search controls. Run Google AI search controls. It narrows the evidence to supported technical eligibility controls per page.

Data sources and inputs

• Saved or fresh crawl report. Provides response, robots, indexability, structured data, entity, and page-structure evidence for each check.
• Fixed check weights and status credit. Define this tool's own scoring, published in the output as an id, version, weight map, and formula.

What this report checks

• Scores the start-URL AI crawler policy, HTTPS, indexable share, structured data, JSON-LD validity, entity sameAs, and opening-content structure.
• Records each check as pass, warn, fail, or unknown, and excludes unknown checks from the score instead of counting them as failures.

How it works

• Scores only the checks with known evidence, weights them, and normalises to 0-100, so unknown checks are excluded rather than counted as failures, and a partial or incomplete crawl cannot reach a clean 100.

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

This report currently uses the shared report runner in the CLI. Agents and CI should inspect the live schema before their first run.

seo reports describe ai-search-scorecard --json

seo reports run ai-search-scorecard --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-search-scorecard"
}

{
  "id": "ai-search-scorecard",
  "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-search-scorecard',
  {
  "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

• A 0-100 heuristic score with per-check observed evidence, a derived finding, and a bounded verification step.
• A partial flag, an excluded list, and the methodology id, version, weights, and formula for reproduction.
• Read the partial flag, the excluded list, and each check before the number. The score is this tool's own heuristic summary, not a search-engine requirement or a visibility verdict.

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

• A blocked AI crawler token can be an intentional publisher choice, and the score never proves indexing, selection, citation, ranking, or traffic.

What to do next

1. Open AI search readiness for the underlying access and structure evidence.
2. Use entity readiness or Google AI search controls for focused follow-up.

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.
• 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.

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
• Google structured data guidance

Browse all reports in AI search evidence.