list_models

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

With no annotations provided, the description carries full burden and does well by disclosing behavioral traits: it describes the comprehensive nature of the returned data (model ID, provider, description, use cases, strengths/weaknesses, sizes, rate limits), which goes beyond a simple list. It doesn't mention pagination or sorting options, but for a zero-parameter tool this is reasonable.

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 perfectly structured: first sentence states the purpose, bullet points detail what's returned, then usage guidance, and finally return format. Every sentence earns its place with zero wasted words, and it's appropriately sized for the complexity.

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?

Given the tool's complexity (discovery-focused with rich output), no annotations, 0 parameters, and the presence of an output schema, the description is complete: it explains what the tool does, when to use it, what information it returns, and references the return format. The output schema will handle the detailed structure, so the description doesn't need to explain return values further.

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 has 0 parameters with 100% schema description coverage, so the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, maintaining focus on the tool's purpose and output.

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 the tool's purpose with specific verbs ('List all available image generation models') and resources ('models with their descriptions'). It distinguishes from siblings like 'get_model_details' (which presumably gets details for a specific model) by emphasizing comprehensive listing of all models.

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 explicitly states when to use this tool ('Use this tool to discover which models are available and choose the best one for your image generation needs') and distinguishes it from alternatives by focusing on discovery rather than specific operations like 'generate_image' or 'get_model_details'.

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