Compare Brand Names

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds valuable behavioral context beyond annotations by specifying what checks are performed (domains, social handles, trademarks, app stores, SaaS channels) and what the tool returns (availability score, safety assessment per candidate, highest-scoring recommendation). This provides important operational details not covered by annotations.

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 perfectly front-loaded with the core purpose in the first sentence, followed by specific checks and return values. Every sentence earns its place with zero wasted words, making it highly efficient for an AI agent to parse.

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, rich annotations, and 100% schema coverage, the description provides good contextual completeness. It explains what checks are performed and what information is returned. The main gap is the lack of output schema, so the description doesn't detail the structure of the 'availability score' or 'safety assessment' return values.

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% with a clear parameter description. The description adds some semantic context by mentioning '2-5 brand name candidates' which aligns with the schema's minItems/maxItems constraints, but doesn't provide additional meaning beyond what the schema already documents about the brand_names parameter.

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 specific action ('compare'), the resource ('brand name candidates'), and the scope ('2-5 candidates side-by-side'). It distinguishes from siblings by specifying comprehensive multi-channel checks (domains, social handles, trademarks, app stores, SaaS channels) rather than single-channel checks like 'check_domains' or 'check_social'.

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 clear context for when to use this tool: when comparing 2-5 brand names across multiple channels. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools (e.g., when you only need to check one channel or want a different type of assessment).

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