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

Annotations already cover read-only/idempotent/closed-world, but the description adds genuinely useful behavior beyond them: results are nearest neighbours of the product's embedding, and there is a fallback to same-category matching when the vector index is unavailable. That fallback materially affects result interpretation. It stops short of noting ranking or result-count behavior.

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?

Two tight sentences with no filler; the core behavior is front-loaded and the fallback caveat follows. Every clause earns its place.

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 an output schema present, return values need not be explained, and the filters are fully covered by the schema. The fallback note closes the main behavioral gap; only ranking/ordering detail is absent.

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 86%, so the schema already documents slug and each filter flag. The phrase 'with optional filters' adds nothing beyond what the schema provides, so the baseline of 3 applies.

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?

States a specific outcome ('products similar to a given one') plus the mechanism ('vector similarity'), which distinguishes it from search_products and compare_products without naming them. It is clear what the tool returns, though it never explicitly contrasts itself with its siblings.

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 implies the use case (finding alternatives to a known product slug) but gives no explicit when-to-use vs search_products or compare_products, and no prerequisites. Usage is inferable rather than stated.

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