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

Annotations already declare read-only, idempotent, non-destructive. Description adds that it handles parameter inference (e.g., '4K' → scale_factor=4), supports multiple languages, and returns ordered model recommendations with wiring, giving behavioral context beyond the safety annotations.

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?

Description is dense but every sentence is purposeful: it states its primary role, capabilities, and integration path, all in a few sentences. It's front-loaded with 'RECOMMENDED FIRST STEP' and avoids redundancy.

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?

Without an output schema, the description explains the return value shape ('models in order, with parameter recommendations and wiring') and its downstream use with create_flow. It covers multi-step chains and language support, making it sufficiently complete for a suggestion tool, though exact output format is not detailed.

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 covers all three parameters with 100% description coverage, so baseline is 3. Description adds contextual info about natural language understanding and parameter inference, but doesn't delve into per-parameter syntax beyond the schema, which already includes examples.

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?

Clearly states it suggests model(s) for processing tasks, with verb 'suggest' reinforced by 'Ask it which model or models to use' and 'returns models in order'. It also differentiates from siblings by positioning itself as the recommended first step before list_models and feeding create_flow.

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?

Explicitly says 'RECOMMENDED FIRST STEP for any image/video/text processing request' and instructs to use before list_models and pass result to create_flow, providing direct when-to-use and chaining guidance.

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