starbucks_product

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It comprehensively covers what is returned (field-by-field nutrition detail), important caveats (no dollar price exposed, only star cost), error behavior (unknown product number or unsupported form returns not found), and the meaning of form and market fields. Took the time to explain the relationship to the nutrition endpoint. Nothing crucial is left to inference.

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 long but not wasteful; every sentence contributes a concrete fact. It front-loads the core purpose ('Get one Starbucks product with nutrition') and then layers return details, params, and caveats. Structure can be improved by splitting into sentences, but it is still an adequate organization for the information density.

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?

This is a read-only tool with no output schema and no annotations, so the description must carry a high burden. It fully covers return contents, dependency chain, error states, and pricing caveat. It also clarifies how this tool feeds the nutrition endpoint, which is valuable context among sibling tools like starbucks_nutrition. I consider it complete for an agent to invoke and interpret the result.

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 schema already defines each parameter. The description meaningfully adds value by describing where product_number comes from, asserting what allowed, explain default_recipe meaning, , and market's default. This is significantly more than a restatement of schema descriptions, so it earns above baseline.

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 it returns one Starbucks product with its full nutrition and product details. It lists exact fields (name, description, product type, image, star cost, customization options, and per-size nutrition), making the tool's purpose highly specific. It does not explicitly name sibling tools, but it references the /starbucks/menu and /nutrition endpoints, which implicitly differentiates it from related tools like starbucks_menu and starbucks_nutrition.

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?

It gives clear guidance on where to source product_number (from /starbucks/menu or product page URL) and states that the returned default_recipe is required by the /starbucks/product/{product_number}/{form}/nutrition endpoint. It also explains what forms are valid and which market to target. It does not explicitly enumerate when not to use alternative tools, but the dependency and sourcing guidance is strong enough for an agent to select and use the tool correctly.

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