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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses return fields, quality grade, PII masking, and fields_hint behavior. Annotations set readOnly and idempotent, consistent with description. Adding a note about the structure of the fields_hint or response format would be helpful but output schema exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured, front-loaded with purpose, uses bullet points for notes, every sentence adds value. Appropriate length for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given single parameter, existing output schema, and annotations, the description sufficiently covers all aspects: purpose, usage, behavior, and integration with the workflow (Step 3, fields_hint, file export).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema coverage, the description fully explains the single parameter: 'execution_id from a completed data_process job (job.status response)', including its origin and format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Read structured fields extracted from a completed document (Step 3)', uses specific verb and resource, and distinguishes from siblings by specifying execution_id comes from data_process jobs only and guiding to dataset.build for file retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use (after completed data_process job), provides source of execution_id, notes that dataset_build jobs use dataset_id, and suggests alternatives (dataset.build, dataset.export).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.