Your automated review calls the Claude API for each pull request, using tool_use with a...

Option A reduces the maximum output required from any single response while preserving the structured schema and complete severity range. The pipeline can partition files into coherent groups, execute bounded reviews, validate each returned array, and merge and deduplicate findings using stable fields such as file path, line number, category, and description.

Anthropic’s stop-reason documentation confirms that max_tokens means generation reached the configured output limit and the response must be treated as incomplete. Structured output constraints can guarantee schema-valid generation when completion succeeds, but they cannot create unlimited output capacity. A large findings array can still exceed the available token budget.

Option B may postpone the failure but provides no durable guarantee for still-larger pull requests, and aggressively shortening descriptions may eliminate necessary evidence. Option C abandons machine-validated structure without reducing the amount of generated content. Option D deliberately suppresses medium- or low-severity findings and repeats an oversized request rather than addressing its scope. Partitioning establishes predictable output bounds, supports targeted retries, retains every required finding category, and prevents a single truncated response from invalidating the complete review.

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