Export Dataset

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

Annotations already declare readOnlyHint and idempotentHint (safe read). Description adds key behavioral details: returns content as UTF-8 string directly, no file download, and binary format limitations. No contradictions.

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?

Efficient single paragraph that front-loads core purpose, then logically covers response format, prerequisites, format guide, and limitations. Every sentence adds value with no redundancy.

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 tool complexity and existing output schema, description fully covers prerequisites, parameter usage, format options, and limitations. No missing information for correct invocation.

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 description coverage, description thoroughly explains both parameters: format (lists each format's use case) and dataset_id (source from completed build). Go beyond schema defaults and types.

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?

Description clearly states the tool's purpose: 'Download all records from a built dataset as text (Step 5 — final step).' It distinctively identifies itself as the retrieval step after building, distinguishing from sibling tools like dataset.build and job.status.

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 provides prerequisites: call get_job_status after build_dataset, wait for status='completed', and use dataset_id from that response. Also advises when not to use (binary formats) and directs to dashboard alternative.

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