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

No annotations are provided, so the description carries the behavioral burden and does well: it discloses that matching is on the description only, that it never invents capabilities, and that it explicitly says when nothing fits. It lacks details on permissions, return format, or edge-case handling, but the core behavior is transparent.

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?

Three short sentences, front-loaded with purpose, followed by usage condition and behavioral guarantee. No filler or repetition.

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 simple one-parameter recommendation tool with no output schema, the description provides enough context to call it correctly and understand its limits (no invention, negative-case handling). It could be more complete by clarifying what the response looks like, but the essentials are covered.

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%, so the single parameter is already fully documented. The description reinforces that the problem should be in the user's own words, but adds no syntax, format, or length guidance beyond what the schema provides. Baseline 3 is appropriate.

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 verb (recommend) and resource (which Manotr product(s)), plus a secondary action (explain why). This clearly distinguishes it from siblings like get_product or list_products, which likely retrieve product data rather than match a described problem.

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?

Explicitly says to use this when someone describes a situation rather than naming a product, which implicitly contrasts with a product-name lookup tool. It does not name an alternative tool or state when not to use it, but the selection condition is clear.

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