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

Annotations already declare readOnlyHint and idempotentHint, so no contradiction. The description adds meaningful behavioral context by outlining the exact return payload (model id, display name, brand, credits), which goes beyond what the annotations provide.

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 a single, well-structured sentence that front-loads the action ('Browse') and efficiently packs the resource, scope, return fields, and usage rationale. Every word serves a purpose.

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 no-parameter, read-only browse tool with strong annotations, the description provides complete context: what it does, what it returns, and why to use it. No output schema exists, but the description adequately covers the return information.

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 zero parameters, so schema coverage is trivially 100%. The description correctly omits parameter details, and according to the baseline for parameterless tools, a score of 4 is appropriate.

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?

Description states the tool browses image-generation models for the user's Switch account and explicitly lists the return fields (model id, display name, brand, credits-per-image). This clearly distinguishes it from sibling video model tools and directly ties it to the generate_image workflow.

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 frames usage as 'before calling generate_image', which provides a clear when-to-use context. It does not name alternative tools for exclusion, but the user's intent to pick a model is well-served by this guidance.

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