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

Annotations already declare read-only, idempotent, non-destructive behavior, so the description only needs to add context. It does by disclosing the order-history origin and the category fallback, which explains the tool's data source and behavior if co-purchase data is absent.

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 sentences, about 25 words total, with the core algorithm and data-source detail front-loaded and the use cases appended. Every sentence adds value and there is no filler.

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 two well-documented parameters, a read-only/idempotent annotation set, and an output schema, the description covers the data source, fallback, and common query intents. There is no additional context an agent needs to select and invoke the tool correctly.

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%; product and limit are already documented with their meanings and default/max values. The description adds no parameter-level detail beyond the schema, so it hits the baseline expectation without needing to compensate.

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 clearly identifies the tool's purpose: recommending products frequently bought together, with a real-order-history source and same-category fallback. It is specific about the resource and behavior, but it doesn't name or contrast sibling tools such as best_sellers, so differentiation relies on context rather than explicit statements.

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 'Use for ...' sentence explicitly tells an agent when to reach for this tool ('what goes with this', 'customers also bought', cross-sell). It does not mention when not to use it or point to a particular alternative, but the intended invocation context is clear.

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