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

With no annotations provided, the description carries the full burden and delivers key behavioral detail: it returns confidence backed by real co-purchase data, and it returns a 'clear no signal' result when no qualifying rule exists. It doesn't discuss read-only guarantees or confidence scale, but the described fallback and data source cover the most important behavioral nuances for a scoring tool.

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 front-loaded sentences with no waste: the first states the operation and output, the second gives usage and example questions. Every phrase earns its place, and the most important information appears immediately.

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?

There is no output schema, so the description must explain return behavior—it does, by promising either a confidence value or a no-signal result. It also covers usage scenarios. It doesn't specify the confidence format or scale, but for a simple two-parameter tool this is a minor gap rather than a completeness failure.

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 100%, with product_a and product_b clearly described as antecedent and consequent. The tool description adds little beyond that, only framing them as 'two specific products.' Baseline 3 is appropriate since the schema already documents the parameters fully.

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 names a specific verb ('score'), resource ('cross-sell strength between two specific products'), and explicitly distinguishes this pairwise scoring from other tasks by giving example questions ('is X a good cross-sell for Y?'). It clearly differentiates from siblings like find_substitutes or get_recommendations without needing to inspect their 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?

It provides concrete usage context: 'Use this to validate a proposed pair before recommending it,' plus example queries that map to user intents. It doesn't explicitly mention when not to use it or name alternative tools, but the guidance is clear enough for an agent to select it for pair-level validation.

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