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

Annotations already declare readOnlyHint=true, destructiveHint=false and closed-world scope, so safety is covered structurally. The description adds genuinely new behavioral context: the deliberate omission of example rows for protected-split safety reasons and the fact that hashes/coverage counts are what surface instead. It stops short of noting pagination, ordering, or empty-version behavior.

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?

Three tight sentences with the return payload front-loaded, followed by the key privacy constraint and then the decision questions. No filler, though the phrasing is slightly compressed/telegraphic ('Frozen versions of a dataset as counts...').

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?

With no output schema, the description does carry the return-shape burden and discharges it (counts, template coverage, hashes) plus what is withheld. For a two-parameter read-only listing tool this is close to sufficient; minor gaps remain around ordering, empty results, and how counts map to individual versions.

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-schema, including the superadmin restriction on customerId. The description adds no parameter-level meaning at all, so the 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?

The description states the resource precisely (frozen versions of a dataset) and enumerates the payload shape (counts, template coverage, hashes), so an agent knows what it is getting. It implicitly distinguishes itself from list_dataset_examples by asserting 'the examples themselves are never returned', but never names that sibling or others such as list_datasets/get_dataset, so differentiation is inferred rather than explicit.

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?

Two concrete decision questions ('is there a frozen version to train on?', 'is it training-eligible?') give clear context for when to call this tool. However, no alternatives or exclusions are named – it never points to freeze_dataset_version or bind_dataset_version, which are the obvious adjacent operations, so the routing burden remains partly on the agent.

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