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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context beyond annotations by specifying the returned information: credit costs, capability limits, and model-specific production guidance. This helps the agent know what to expect from the call.

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 two sentences with no filler. It front-loads the core purpose, then efficiently adds disambiguation and alternative routing. Every sentence earns its place.

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 simple, one-parameter, read-only list tool with full schema coverage and clear annotations, the description is complete. It tells the agent what the tool returns, how to categorize models, and which sibling to use instead. No critical information 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?

The schema already documents the only parameter, model_type, with a clear description covering its allowed values ('generation, edit, upscale, video, backdrop'), so schema coverage is 100%. The description adds context about model categories but does not need to repeat the schema details.

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 clearly states a specific verb and resource: 'List available AI generation models... by type', and explicitly distinguishes these AI models from human models/avatars. It also names the sibling tool (list_avatars) for the alternative, making the tool's purpose unmistakable.

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?

It gives explicit usage guidance: this tool is for AI generation models, not human models/avatars, and directs users to list_avatars for those. This is a clear when-to-use vs alternative distinction.

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