interpret_pricing_model

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

With no annotations, the description takes on the full burden. It discloses unique behavioral traits: returns a favorability assessment, the key rule, and a counter pattern, and notably states 'No absolute rates — anchor to your own base,' which is a critical usage caveat. It does not describe any side effects or hidden limitations, but for a read-only interpret tool, this is sufficient.

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 long, front-loads the action verb, and every clause adds meaningful information. There is zero filler or redundant phrasing.

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 single-parameter tool with no output schema, the description is fully complete: it specifies the input domain, the exact type of output (three components), and the crucial behavioral constraint about absolute rates. An agent can invoke this tool correctly without further clarification.

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 input schema has 0% description coverage, so the description must compensate. It does by listing all enum values (flat_fee, cpm_based, performance_based, per_click_sponsorship, retainer) and explaining that the parameter is the pricing model to interpret. It also adds the semantic context that results are relative to the user's own base, which goes beyond the raw schema.

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 action ('Interpret a pricing model') and the specific resource (pricing model in a creator deal), listing all accepted model types. It also distinguishes the tool from siblings like interpret_usage_term by focusing specifically on pricing models and describing concrete outputs (when it favors you, key rule, counter pattern).

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 implies when to use the tool: whenever you have one of the listed pricing model types in a creator deal and need an interpretation. It does not explicitly exclude alternatives or mention sibling tools, but the context is clear enough for an agent to select it over other interpret tools. Lack of explicit 'vs. alternatives' wording prevents a 5.

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