starbucks_menu

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so well: it reveals the output structure, the fields returned, the availability semantics of store scoping (NotAvailableHere vs Available), and meaningful differences between the two markets. It does not discuss pagination, response size limits, or error behavior, but for a menu-browsing tool the disclosed behavior is substantially complete.

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 not bloated; every sentence adds operational value, and the main purpose and return shape are front-loaded. It is long because it covers two non-obvious parameters thoroughly, but it stays on-topic.

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?

Despite having no output schema, the description explains the return structure, the product fields, how to pair results with a sibling tool, how store scoping changes availability, and what market values are valid. An agent has enough information to select this tool, choose parameters, and interpret the response.

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?

Even though schema coverage is 100%, the description goes far beyond the schema by explaining what 'market' actually changes (282 US products vs 253 CA, exclusives on both sides, only these two supported) and what 'store_number' does to availability (marks items NotAvailableHere). This is exactly the kind of contextual meaning an agent needs to choose parameter values correctly.

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 uses a specific verb ('Browse') and concrete resource ('the full Starbucks menu') plus describes the exact output shape: a category tree with top-level categories, child categories, and products with detailed fields. It also explicitly separates this tool from starbucks/product by saying to pair menu output with that endpoint for detail, so an agent can disambiguate without opening schemas.

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 states exactly when to use this tool (browsing the full menu) and points to the alternative for deeper data ('Pair a product's product_number and form with /starbucks/product to fetch full detail including nutrition'). It also gives clear selection guidance for both optional parameters, including that only 'us' and 'ca' markets are valid and that European sites are unavailable, which prevents misrouting.

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