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

No annotations provided, so description carries full burden. It discloses the ranking method (not ads/affiliates), return fields (share %, trend, per-engine breakdown), and fuzzy matching behavior. Could mention authentication or rate limits but not critical.

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?

Single paragraph, front-loaded with key value. A bit long but every sentence adds necessary information. Could be slightly more concise but overall well-structured.

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?

No output schema, but description explains return values: ranked products with share %, trend, per-engine breakdown. Covers parameters and usage well. Good completeness for the complexity.

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 100% means baseline 3. Description adds meaning: explains fuzzy matching for 'category', default/max for 'limit', and that 'constraints' are echoed back not applied. Adds value beyond schema.

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 provides software recommendations based on measured AI answer data, for a specific category or job. It distinguishes from siblings by focusing on ranking by AI recommendations rather than ads or affiliates.

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 'Use when asked...' with examples like 'what software/tool should I use for X'. Mentions fuzzy matching and near-miss inputs return suggestions. No explicit when-not-to-use but context is clear.

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