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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description still adds value beyond them: 'Cost: free,' 'no user data' in the payload, and the important non-obvious behavior that the ai-influencer variant returns tiers on the first call and only expands detail once tier is passed.

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?

Front-loaded with the core read statement, then usage, cost, per-product notes, and returns — a logical order with no filler sentences. It is telegraphic and slightly dense, packing three product contexts into one line, but every clause carries information.

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?

There is no output schema, and the description covers the return shape at a high level ('model payload (no user data)'), which is adequate but not detailed on field structure. For a one-required-parameter read tool with conditional per-product behavior, the definition covers what an agent needs to call it correctly in each product context.

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?

Schema coverage is 100%, so the baseline is 3, and the description earns an extra point by explaining conditional parameter roles the schema only states flatly: tier and category are ai-influencer specific, presets is genjutsu-only, and tier is required to move from tier listing to catalog detail. It stops short of syntax or format examples for model_id beyond the schema's own slug example.

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: 'full public config for one catalog model by slug.' It also implicitly distinguishes itself from list_models by scoping to 'one catalog model,' so an agent can tell the single-model read apart from the enumeration sibling without opening either schema.

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?

Explicit 'When to use: after list_models to confirm modes, qualities, and options,' plus concrete product-specific routing: ai-influencer lists tiers on first call then takes tier/category for catalog detail before generate_character_sheet, and genjutsu uses presets=true. This is real when-to-use guidance tied to named sibling tools.

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