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

With no annotations provided, the description carries the full disclosure burden and delivers substantive context: it reveals that blend identity is not a named field but is encoded in the variant SKU, and warns that 'a product's title can say one thing while its SKU is really a different blend' — a genuine data-model trap. It also discloses the return scope ('in full, with all its variants'). It does not cover error behavior or auth requirements, but for a read operation there is little hidden destructiveness to disclose.

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 purpose is front-loaded in a single sentence, and the second paragraph, while detailed, earns its length because the SKU example ('COF-WHB-12O-HGL-BOX' → 'HGL' → High Lakes) and the title/SKU mismatch warning are load-bearing for correct downstream use. There is no filler and no restatement of schema fields.

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?

With one required parameter and an output schema present to cover return-value details, the description covers everything an agent must know to use the result correctly: purpose, full-return scope, and the critical SKU-decoding trap that could cause wrong origin/roast copy. The only shortfall is the opaque dpop_token parameter, which neither the schema nor the description explains.

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 67%, so the baseline of 3 applies: the schema already documents npub for credit billing and product_id as coming from list_my_products, and the description adds no parameter-level detail on top. The SKU guidance concerns the returned data rather than the inputs, and dpop_token remains unexplained in both the schema and the description, leaving a small uncompensated gap.

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 first sentence states a specific verb and resource: 'Get one of your saved product designs in full, with all its variants.' The phrase 'your saved product designs' distinguishes it from siblings like roastify_get_catalog_product and roastify_get_blend, and 'one of' contrasts with roastify_list_my_products, 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?

The 'saved product designs... in full, with all its variants' framing gives clear context for when this tool applies versus catalog/blend/list siblings, and the input schema reinforces that product_id comes from list_my_products. The SKU-decoding paragraph is a concrete post-call guideline ('Decode the SKU before writing origin/roast copy'). It stops short of explicitly naming alternatives or when-not-to-use conditions, so it misses a 5.

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