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

Beyond the readOnly and idempotent annotations, the description discloses the exact return structure, explains the meaning of a false 'valid', and calls out the embedded-mode special case where no hash chain exists. This is valuable behavioral detail the annotations do not provide.

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 brief, front-loaded with the core action, and every sentence earns its place. It provides the essential behavior, the return shape, the interpretation, and an important edge case without wasted words.

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?

The tool has only one documented parameter, output schema is available, and the description covers both the normal behavior and the embedded-mode special case. An agent has enough context to invoke the tool correctly and to interpret its result.

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 the single 'id' parameter is already described as 'The model id.' The description reinforces that this id refers to a specific model's audit trail but adds no new semantic detail beyond the schema.

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 names a specific verb ('Verify'), a specific resource ('tamper-evidence hash chain of a model's durable audit trail'), and explains the result semantics. This clearly distinguishes it from sibling tools like get_audit or get_history, which would retrieve rather than verify.

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?

The description clearly conveys what the operation checks, and the embedded-mode caveat adds useful context. However, it never explicitly tells an agent when to choose this over get_audit or get_history, so the usage guidance relies on inference rather than direct instruction.

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