List Available Models

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

No annotations are provided, so the description carries the disclosure burden. It does state the returned fields (speed, quality, resolution support, best use cases), which is useful, but says nothing about whether the list is static, requires auth, or is cached/rate-limited — modest coverage for an unannotated tool.

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 short sentences, front-loaded with the verb and resource, and each adds information (what's returned, why to call it). Slightly verbose on the return-value recap given an output schema exists, but no real waste.

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 zero parameters and an output schema present, the description need only justify when to use the tool, which it does. The remaining gap is minor: no guidance on what to do with the returned model identifiers before calling generate_image.

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?

The tool takes zero parameters, so there is nothing to disambiguate and the baseline is 4. The description correctly implies a parameterless, no-argument call.

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 and resource ('List all available image generation models') plus what the results contain. Sibling tools like list_templates and list_history clearly cover different resources, so the agent can distinguish it without reading schemas, though no sibling is named explicitly.

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?

Provides clear usage context: 'Use this to pick the right model for your task,' which tells the agent when to call it relative to the generation workflow. It stops short of explicit exclusions or naming which sibling to call instead once a model is chosen.

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