get_recommendations

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

No annotations are present, so the description carries the behavioral transparency burden. It transparently explains the recommendation source ('mined order-history association rules') but does not explicitly state whether the call is read-only, what response shape to expect, or any platform-specific behavior beyond broad compatibility.

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 core purpose is front-loaded, followed by concrete query examples and a platform-support note. The trigger list is slightly long but earns its place by helping agents recognize equivalent user requests.

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 two-parameter tool with no output schema, the description is reasonably complete: it names the algorithm, scope, trigger phrases, and supported platforms. It does not describe the response format, but the simple recommendation semantics make that a minor gap.

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%, and the input schema already documents both product_id and limit with defaults and accepted formats. The description adds little beyond the schema, mostly reinforcing that product_id identifies the product for cross-sell recommendations.

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 and resource: it recommends complementary, frequently-bought-together products for a given product, based on association rules. It also explicitly positions itself as the single-product cross-sell tool, which distinguishes it from sibling bundle and substitution tools.

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 provides clear trigger examples such as 'what goes with X?' and 'recommend products to cross-sell with X', making usage conditions easy to match. It does not explicitly name alternatives to avoid or say when not to use this tool, so it stops short of full exclusion guidance.

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