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

Annotations already declare readOnly, idempotent, non-destructive, and openWorld behavior, so the safety bar is met. The description adds genuinely new context: a hard cap of 10 results, the exact result fields (vendor, center-to-center size, overall length, finishes, list prices, variant ids, URLs), and the matching policy (exact size/finish first, then nearest sizes) — the latter materially affects result interpretation.

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 tight sentences with the core action and examples front-loaded, followed by return shape and matching semantics in a logical order. The embedded example list is slightly dense but each element carries real information for query construction.

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 single-parameter search tool with no output schema, the description compensates well by summarizing returned fields, the result limit, and fallback matching. The main remaining gap is disambiguation from the similarly named sibling 'search', which matters for correct tool selection.

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% and the parameter is already described in the schema, so baseline is a 3. The description earns above baseline by supplying sample query strings and disclosing the exact-then-nearest matching behavior, which tells the agent how permissive it can be when composing the query.

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 and resource ('Search the live Knobs.co catalog') and enumerates the searchable dimensions (item type, size, finish, brand, series) with concrete query examples. It doesn't distinguish itself from the sibling tools 'search' or 'get_product', so an agent must infer which retrieval path to take.

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?

Usage is shown only implicitly through three sample queries, which convey the expected query shape but not when to prefer this tool over 'search', 'fetch', or 'get_product'. There is no exclusion or alternative-routing guidance, leaving sibling selection to inference.

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