cvs_product_ingredients

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

No annotations are provided, so the description carries the full burden. It discloses several behavioral traits: the data source (dedicated ingredients page, separate from main product endpoint), the scope limitation (non-prescription retail only), the possibility of empty fields for some products (upstream having no label data, not an error), and the US-egress requirement with upstream error behavior. This is strong behavioral disclosure, though it doesn't detail the exact response structure or pagination (which is less relevant for a single-product lookup).

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 well-organized, front-loading the core purpose and content, then covering scope, parameter format, edge cases, and regional constraints. Every sentence adds information. It is longer than the minimum, but the length is justified by the number of important behavioral caveats (empty fields, egress restrictions, URL acceptance).

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 lookup tool with no output schema, the description covers the input format thoroughly, the scope, the data source, edge cases, and failure modes. It doesn't describe the exact return shape, but with no output schema and a simple resource, the description provides enough for an agent to call it correctly and interpret results. The only minor gap is not specifying whether the response is JSON with named fields, but the description's enumeration of content (active, inactive, free-text paragraph) implies the structure.

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 description coverage is 100%, so the schema already documents the slug parameter. The description adds significant value by explaining what a slug is (segment after '/shop/'), providing a concrete example, and noting that a full URL is also accepted. This goes beyond the schema's minimal description and helps the agent construct valid input.

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 states a specific verb ('Returns'), a specific resource (CVS.com OTC product ingredient/label statement), and enumerates the exact content (active ingredients, inactive ingredients, vendor free-text paragraph). It also explicitly distinguishes this from the main product detail endpoint and from prescription/pharmacy data, which differentiates it from sibling tools like cvs_product and cvs_search.

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 OTC product ingredient/label data), what it does NOT cover (prescription items, pharmacy ordering, patient-specific data), and how it differs from the main product detail endpoint. It also provides guidance on the slug format and URL acceptance, plus a note about empty results for non-consumable goods. This is comprehensive usage guidance.

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