---
title: "AI search scorecard | SEO Skill"
description: "Score crawl-based AI search readiness across access, indexability and page signals. Open every contributing check before trusting the number."
canonical: "https://seoskill.dev/docs/reports/ai-search-scorecard"
language: "en"
---

# AI search scorecard

Turn a fixed set of crawl checks into a comparable readiness score. The number summarises the included evidence, so it cannot predict mentions, citations or traffic.

## Install and run the report

Install the CLI once, then run `seo start` to connect your site and Google data. After setup you can run this report yourself or ask your agent to run it.

Install the SEO CLI globally.

```
npm i -g seo
```

Run setup after installation.

```
seo start
```

Run score ai search readiness through the report catalog.

```
seo reports run ai-search-scorecard --params '{"reportId":"crawl_example_20260710"}' --json
```

## What you get from this report

Score crawl-based AI search readiness across access, indexability and page signals. Open every contributing check before trusting the number.

Best for

You want a compact scored summary of the AI-search evidence the crawler already collects.

Needs

Saved or fresh crawl report

You get

Score crawl-based AI search readiness across access, indexability and page signals. Open every contributing check before trusting the number.

Use next

Check AI search technical readiness

Every report follows the same rules for missing data and outside estimates.[See how report data works](https://seoskill.dev/docs/reports#how-report-data-works).

## What you need before you run it

The report uses the information below. Optional sources are only used when you ask for them.

### 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.

## Use a different report for these jobs

These jobs need a different report or a manual review.

[

### You want the underlying access, indexability, and snippet evidence rather than a single number.

Run AI search readiness. It returns the evidence-only assessment this scorecard summarises, with no aggregate score.

AI search readiness audit

](https://seoskill.dev/docs/reports/ai-readiness)[

### You need Google-specific crawl, index, canonical, and snippet controls for AI feature eligibility.

Run Google AI search controls. It narrows the evidence to supported technical eligibility controls per page.

Google AI search readiness audit

](https://seoskill.dev/docs/reports/geo-gaps)

## Use the report with an agent or in code

The command above is enough for most work. These options let an agent, script or application run the same report with explicit input and structured output.

### CLI

Use the report catalog when an agent, script or CI job needs predictable JSON instead of the normal terminal output. Check the current input first, then run the report with the validated parameters.

Check the current input before the agent or script runs the report.

```
seo reports describe ai-search-scorecard --json
```

Once the input is valid, run the report through the report catalog.

```
seo reports run ai-search-scorecard --params '{"reportId":"crawl_example_20260710"}' --json
```

### MCP

An MCP agent follows the same two-step flow. It inspects the current schema, fills the required fields and only then runs the report.

Ask the MCP server for the current report input.

```
{
  "id": "ai-search-scorecard"
}
```

Run the report after the agent has checked and filled the input.

```
{
  "id": "ai-search-scorecard",
  "params": {
    "reportId": "crawl_example_20260710"
  }
}
```

### TypeScript

Install the package in a Node 22 project when your application needs to run the report directly. The result includes the same evidence, warnings and limits returned through the CLI and MCP server.

Add the SEO package to your project.

```
npm install seo
```

Run the report from your TypeScript application.

```
import { executeReport } from 'seo/mcp'

const result = await executeReport(
  'ai-search-scorecard',
  {
  "reportId": "crawl_example_20260710"
},
)

console.log(result)
```

## What to do next

Open the checks behind the weakest score group and fix only the failures supported by crawl evidence. Run the scorecard again against a comparable crawl so the before and after numbers describe the same scope.

1.  01Open AI search readiness for the underlying access and structure evidence.
2.  02Use entity readiness or Google AI search controls for focused follow-up.

## Related reports

[

Check whether crawlers can access and use important pages in AI search. Find technical blockers without inventing a visibility or citation score.

AI search readiness audit

](https://seoskill.dev/docs/reports/ai-readiness)[

Check the crawl, index, canonical and snippet controls used for Google AI search eligibility without predicting whether a page will be selected.

Google AI search readiness audit

](https://seoskill.dev/docs/reports/geo-gaps)[

Check whether schema, authorship and brand details clearly connect the people, organisation and content across your site and find conflicting signals.

Entity SEO audit

](https://seoskill.dev/docs/reports/entity-readiness)

## Sources used by this report

These primary sources define the provider data or search controls used by the result.

-   [Google guidance for AI features and websites](https://developers.google.com/search/docs/appearance/ai-features)
-   [Google robots.txt guidance](https://developers.google.com/search/docs/crawling-indexing/robots/intro)
-   [Google robots meta and snippet controls](https://developers.google.com/search/docs/crawling-indexing/robots-meta-tag)
-   [Google structured data guidance](https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data)