Fix Canonical for AI Input Validation Errors
Canonical for AI depends on clean, well-formed inputs. If inputs are malformed, the report may produce warnings or false negatives. Validate structure before running production checks.
Common causes
- Malformed URLs, headers, JSON-LD, or robots directives.
- Missing required fields or invalid data types.
- Using staging/demo placeholders instead of live production input.
How to fix
- Validate syntax and required fields before submission.
- Replace placeholders with real production values.
- Use the related checker tools to pre-validate input blocks.
Common errors
ValidationError: Invalid input payload in canonical-for-aiFetchError: Timeout while requesting target URL for canonical-for-aiParseError: Unsupported response format detected by canonical-for-ai
FAQ
- Why does Canonical for AI return weak results?
- Weak results usually indicate missing baseline signals (crawlability, schema, or clean input). Validate prerequisites and rerun the check.
- How do I improve Canonical for AI reliability in production?
- Use stable URLs, valid structured data, and consistent machine-readable files. Re-test after each fix to confirm signal improvement.
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