list_ai_models

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

With no annotations provided, the description is the only source of behavioral context. 'List' conveys a read-only operation and 'available' suggests a catalog lookup, but the description does not mention result shape, pagination, or whether results are account-scoped. This is adequate but leaves clear gaps.

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 one focused sentence with no filler. The verb and object are front-loaded, making the purpose immediately 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-only tool, this is close to sufficient, but the absence of an output schema and any detail about what information is returned for each model leaves an agent guessing about the response. It is adequate for selection but not fully complete.

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% because account_id already has a description in the input schema. The tool description adds no further parameter semantics, so the baseline score 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 uses a specific verb ('List') and a specific resource ('available Workers AI models'), so an agent can identify the tool's domain. It does not explicitly contrast it with related siblings such as list_workers or list_ai_gateways, so it stops short of the highest score.

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?

No guidance is given about when to use this tool over related alternatives like list_workers, list_worker_services, or list_ai_gateways. There are no exclusions, prerequisites, or contextual hints beyond the verb 'List'.

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