Capture the rendered page and request documented metadata, summary, and quality fields. Compare title and description intent with visible content, then verify canonical, robots, social tags, and schema through specialist parsers.
POST /api/v3/analyzeSNAPSHOT_SITE_API_KEY
AI metadata analysis should connect machine-readable fields with visible page intent. Snapshot Site can add rendered-page summary and metadata context, but canonical selection, robots directives, Open Graph, Twitter Cards, and JSON-LD validity still require direct document parsing and standards-aware validation.
Compare the declared title and description with the visible page topic rather than optimizing isolated strings.
Preserve the screenshot used during review; personalization or blocked content can make metadata appear misleading.
Use a parser to inspect canonical, robots, social cards, hreflang, and structured-data syntax.
Metadata quality does not prove rankings or click-through performance. Measure outcomes after deployment.
Select representative URLs and intended primary topics
Render and request summary plus metadata context
Parse technical tags with deterministic validators
Route mismatches to an editor or engineer
Use a trusted backend, validate the response, and preserve evidence before downstream processing.
curl --request POST \
--url https://api.prod.ss.snapshot-site.com/api/v3/analyze \
--header 'Content-Type: application/json' \
--header 'x-snapshotsiteapi-key: YOUR_API_KEY' \
--data '{
"url": "https://example.com",
"width": 1440,
"fullSize": true,
"waitForDom": true,
"enableSummary": true,
"enableQuality": true
}'
AI metadata analysis should connect machine-readable fields with visible page intent. Snapshot Site can add rendered-page summary and metadata context, but canonical selection, robots directives, Open Graph, Twitter Cards, and JSON-LD validity still require direct document parsing and standards-aware validation.
Metadata review is credible when every recommendation cites the observed tag, rendered page context, canonical state, and rule that triggered concern. Avoid generic optimization prose without a source value.
Compare the declared title and description with the visible page topic rather than optimizing isolated strings.
Preserve the screenshot used during review; personalization or blocked content can make metadata appear misleading.
Use a parser to inspect canonical, robots, social cards, hreflang, and structured-data syntax.
Metadata quality does not prove rankings or click-through performance. Measure outcomes after deployment.
Save the requested URL, resolved canonical, title and description values, social tags, render timestamp, screenshot, generated observation, and final editor decision. This preserves both the machine signal and the human correction.
The analyze endpoint's metadata response is not a complete standards validator or search-console replacement. Parse source tags directly and validate structured data with tools built for those formats.
AI can flag mismatch or ambiguity, but it cannot know search demand, approve brand language, or guarantee click performance. Validate lengths and presence deterministically, then let an editor judge meaning.
Metadata may expose embargoed copy or private preview URLs. Run checks behind a trusted service, redact tokens, and limit who can open the evidence attached to unpublished pages.
Connect AI SEO analysis, AI content extraction, and the API documentation for an evidence-based metadata workflow.
No. Titles and descriptions interact with query intent, search-engine rewriting, brand recognition, position, and competition. Measure deployed outcomes.
Use a JSON-LD or structured-data validator for syntax and required properties. AI can help explain findings but should not replace deterministic parsing.
Yes, the rendered summary and screenshot provide context for editorial review. A person should confirm consequential changes.
Pages with distinct intent should have useful, specific metadata. Do not manufacture variations for duplicate or canonicalized pages.
Review page metadata beside rendered screenshots and AI-assisted content context. Detect inconsistencies for human review without inventing search-performance claims.
Use screenshot for browser-rendered assets, analyze for the documented summary and quality response, and compare for before-and-after visual evidence. Add external tools for requirements outside those contracts.
Yes, through an external scheduler such as cron, CI, serverless scheduling, or an automation platform. Snapshot Site does not claim a native scheduler.
Keep source, request settings, timestamp, raw response, screenshot or diff, and the owner decision together. Do not act automatically on consequential AI output.
Start with a small authorized page set, preserve every input and artifact, and verify the review process before adding volume.