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

Annotations already mark this as read-only and idempotent, and the description adds meaningful behavioral detail: masked PII placeholders are never raw, the response may include a fields_hint when no dataset has been built, and execution_id is restricted to data_process jobs. These details help the agent predict responses and downstream actions beyond the schema.

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?

The description is longer but each sentence adds workflow or behavioral value. It is front-loaded with the core purpose, followed by usage notes and parameter clarification. Minor redundancy exists between the 'Use the execution_id...' sentence and the later note about execution_id provenance, but the structure remains clear and scannable.

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?

For a single-parameter read tool with an output schema, the description covers the essential context: what data comes back, what happens when no dataset has been built, how to get rows as a file, and which job types are valid. Nothing an agent needs to call this tool correctly or route to related tools is missing.

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?

Schema coverage is 0%, but the description's Args section defines execution_id as 'Execution ID from the job.status completed response.' This adds necessary provenance semantics on top of the bare integer type. It also clarifies the execution_id vs dataset_id distinction, which is important for correct invocation.

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 starts with a specific verb and resource: 'Read structured fields extracted from a completed document (Step 3).' It clearly identifies what the tool returns and distinguishes it from dataset.* and document.* siblings by framing it as a job-result/read step. The Step 3 positioning helps an agent place it in the workflow without ambiguity.

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?

It explicitly tells the agent where to get execution_id: from a completed data_process job's job.status response. It also gives exclusions and alternatives: dataset_build jobs use dataset_id, and to retrieve all rows as a file the agent should proceed to dataset.build → dataset.export. This is strong routing guidance.

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