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

Adds substantial context beyond annotations: irreversibility, the 409 abort-on-violation behavior, the blocked-for-training marking on diversity-floor failures, that only counts/coverage/hashes are returned (never examples), and the approve-scope stamping of the key id. The non-destructive annotation is consistent with creating a new version rather than deleting data, so no contradiction.

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?

Dense but front-loaded: purpose and scope lead, then failure semantics, then output and auth constraints. Every clause carries information an agent needs; nothing is padding.

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 mutation tool with no output schema, the description covers the mutation semantics, error behavior, partial-success case, what is returned, and the authorization scope. Nothing needed to call it correctly 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 description coverage is 100% and both parameters (datasetId, customerId) are documented in the schema, so the description adds no parameter-level detail. Baseline 3 applies.

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?

States a specific verb (Freeze) and resource (an immutable, numbered dataset version), and situates it distinctly from siblings like list_dataset_versions and bind_dataset_version by naming it as 'the one approval Datasets owns, and the version training binds to.' An agent can distinguish it without opening the schema.

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?

Clear context: use it to create the immutable version training binds to, with the explicit caveat that it aborts on a violated leakage check and freezes-but-blocks versions under the diversity floor, plus a hard SCOPE: approve restriction naming which keys cannot call it. It does not explicitly name an alternative or a 'when not to use,' so it falls short of the top tier.

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