AI prompt observation report

See what a small set of AI models returned for the questions that matter now. Every answer keeps its prompt, model, settings, citations, cache state and exact cost, so later changes can be compared without inventing a visibility score.

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.

Runafter install

Install the SEO CLI globally.

npm i -g seo

Run setup after installation.

seo start

Record one fixed prompt against an exact current model.

seo reports run ai-prompt-observations --params '{"prompts":[{"id":"analytics-tools","prompt":"Which privacy-friendly analytics tools suit a small publisher?"}],"models":[{"surface":"chatgpt","model":"current-model-name"}],"target":{"label":"Example Analytics","domains":["example.com"]},"countryCode":"GB","languageCode":"en"}' --json

What you get from this report

Check fixed prompts against exact current AI models, retain answer and citation evidence locally, and compare only compatible observations over time.

Best for: You need the current answer, mentions, or citations for a small decision-critical prompt set.
Needs: Fixed prompts and exact model names
You get: Check fixed prompts against exact current AI models, retain answer and citation evidence locally, and compare only compatible observations over time.
Use next: Research AI mentions and citations

Every report follows the same rules for missing data and outside estimates.See 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.

Fixed prompts and exact model names

Defines up to five prompts and four current ChatGPT, Claude, Gemini, or Perplexity models. The free model catalog is checked before paid work.

Named target and competitors

Supplies aliases and domains used to find answer mentions and citations without relying on provider-specific target fields.

Optional Search Console query rows

Adds limited Search Console query and landing-page context for a property you own.

Live AI response evidence

Provides answer text, citations, extra supporting searches returned as fan-out queries, the model the provider actually ran, token use, task id, cache state, and exact returned cost.

Use a different report for these jobs

These jobs need a different report or a manual review.

You want broader provider-indexed mention counts, cited domains, or related question samples for one market.

Run AI mention research. It queries a limited indexed dataset instead of generating a fresh answer for every supplied prompt and model.

AI mention research report

You need a prompt set grounded in searches already associated with the site.

Build a stable prompt seed set from returned Search Console queries first, then choose a small decision-critical subset for live observation.

AI search query report

You need measured visits from known AI products.

Run AI referrals. A generated answer or citation does not prove that a visit reached the site.

AI referral traffic report

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.

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-prompt-observations --json

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

seo reports run ai-prompt-observations --params '{"prompts":[{"id":"analytics-tools","group":"commercial","prompt":"Which privacy-friendly analytics tools suit a small publisher?"}],"models":[{"surface":"chatgpt","model":"current-model-name"}],"target":{"label":"Example Analytics","aliases":["Example"],"domains":["example.com"]},"competitors":[{"label":"Competitor Cloud"}],"countryCode":"GB","languageCode":"en","site":"sc-domain:example.com"}' --json

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-prompt-observations"
}

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

{
  "id": "ai-prompt-observations",
  "params": {
    "prompts": [
      {
        "id": "analytics-tools",
        "group": "commercial",
        "prompt": "Which privacy-friendly analytics tools suit a small publisher?"
      }
    ],
    "models": [
      {
        "surface": "chatgpt",
        "model": "current-model-name"
      }
    ],
    "target": {
      "label": "Example Analytics",
      "aliases": [
        "Example"
      ],
      "domains": [
        "example.com"
      ]
    },
    "competitors": [
      {
        "label": "Competitor Cloud"
      }
    ],
    "countryCode": "GB",
    "languageCode": "en",
    "site": "sc-domain:example.com"
  }
}

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-prompt-observations',
  {
  "prompts": [
    {
      "id": "analytics-tools",
      "group": "commercial",
      "prompt": "Which privacy-friendly analytics tools suit a small publisher?"
    }
  ],
  "models": [
    {
      "surface": "chatgpt",
      "model": "current-model-name"
    }
  ],
  "target": {
    "label": "Example Analytics",
    "aliases": [
      "Example"
    ],
    "domains": [
      "example.com"
    ]
  },
  "competitors": [
    {
      "label": "Competitor Cloud"
    }
  ],
  "countryCode": "GB",
  "languageCode": "en",
  "site": "sc-domain:example.com"
},
)

console.log(result)

What to do next

Open the exact answer, citations, effective model, cache state and returned cost before acting on a match. Validate repeated themes against keyword, result and existing page evidence before planning content or a programmatic template.

01Inspect the full answer, citations, effective model, cache state, and cost before acting on a target match.
02Validate repeated themes with keyword metrics, current search results, and existing page evidence.
03Check source rights, stable identifiers, field coverage, missing values, useful variation, and representative output before scaling a programmatic template.
04Repeat only prompts that matter, using the same exact configuration. Start a new baseline when the effective model changes.

Compare provider-indexed AI mentions, cited domains and question samples with optional Search Console evidence for one exact surface and market.

AI mention research report

Turn real Search Console queries into repeatable AI monitoring prompts so assistant answers can be checked against demand your site already sees.

AI search query report

Compare search volume, trends, CPC, competition, difficulty, intent and result counts for a list of keywords in one country and language before deeper research.

Keyword metrics report

Inspect exact organic ranks, domains, URLs, titles, snippets and result features for one keyword, location, language and device-specific search snapshot.

Live SERP results report

Research ways to expand an existing programmatic page system with Search Console, keyword ideas, current results and competitor pages before building more.

Programmatic SEO opportunities

Find visits from known AI assistants in Google Analytics, see which landing pages received them and keep missing referrers separate from confirmed traffic.