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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by explaining that the tool returns a specific example output format, that an empty query with an area set browses that area, and most importantly, that it never raises protocol errors but returns an error object—a critical behavioral detail not deducible from annotations. The only minor gap is that it doesn't describe any rate limits or authentication needs, but those are not indicated as issues by context signals.

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 description is highly concise at about 100 words, with every sentence serving a distinct purpose: stating scope and example, usage guidance, error handling, and idempotency. It is front-loaded with the core action and key constraint (free, 138 products), making it efficient for an agent to parse quickly.

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?

Given the tool's moderate complexity (3 optional parameters, no nested objects, no enums), the description provides a complete picture: purpose, input example, output format, usage boundaries, error behavior, and safety guarantees. An output schema exists, so the description does not need to detail return values. The coverage is thorough for an AI agent to select and invoke this 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%, so the baseline is 3. The description adds value by providing a concrete example of a typical input and demonstrating how the parameters work together (e.g., 'empty query with an area set browses that area'), which helps an agent understand parameter interplay beyond the schema's individual descriptions. It also explains the default limit behavior and clamping in the schema, but the description reinforces this with context. It does not introduce new parameter details, but the example compensates.

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 states the tool searches all 138 catalog products by keyword and/or focus area, which is a specific verb+resource combination. It distinguishes itself from siblings like list_kits and recommend_products by explicitly naming them and stating what they do differently, making the purpose unambiguous and differentiating it effectively.

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 explicit guidance on when to use this tool (when the caller names a keyword, product, or focus area) and when not to use it (not for bundles, use list_kits instead; not for plain-language problem statements, use recommend_products). It names two alternatives, giving clear context for selection.

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