starbucks_nutrition

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

With no annotations, the description fully discloses key behaviors: it returns a four-value panel, rejects empty modifier lists, errors on invalid product numbers, and explains the complete-build semantics. It also notes the return is smaller than the standard per-size panel, providing comprehensive behavioral context.

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 dense but every sentence adds critical information: purpose, supported products, parameter details, error behavior, and return value. It is logically ordered from high-level purpose to specific constraints, with no fluff.

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?

Given the nested request object, lack of output schema, and no annotations, the description covers all necessary details: how to construct modifiers, how to obtain size_sku, the error on empty lists, and the exact return panel. An agent has everything needed to call the tool correctly.

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?

While the schema describes each parameter, the description adds substantial meaning: it maps product_number/form to specific drinks, explains that size_sku comes from /starbucks/product sizes[].sku, and details the modifier structure (sku, quantity, replaced_sku). This goes far beyond the schema's brief descriptions.

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 tool recalculates nutrition for a customized Starbucks drink, specifying the exact outputs (calories, fat, sugars, protein) and contrasting with the standard recipe. It also names the four supported beverages, distinguishing it from starbucks_product which returns the standard build.

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 states when to use this tool (for customized builds) and implicitly when not to (for standard builds, use /starbucks/product). It also specifies the exact product/form combos and explains the complete modifier build requirement, leaving no ambiguity about invocation.

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