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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. Description adds behavioral details: probes each entity with ai_visibility_check, ranks by score, returns ranked list with score, confidence, signal density. Explains API key usage for Anthropic model.

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?

Two sentences plus an example and output mention. Every sentence adds value. Front-loaded with key action and purpose. No redundant or unclear phrasing.

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 4 parameters and no output schema, the description explains purpose, internal mechanism, output format, and parameter roles. Could mention output schema details, but sufficient for agent understanding.

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%. Description adds additional meaning: first entity treated as 'subject', models include defaults and Anthropic requiring key, context disambiguates common names. Goes beyond basic schema descriptions.

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?

Description clearly states the verb 'compare... side-by-side', the resource 'AI visibility across multiple entities', and the output ranking with scores. It distinguishes itself from sibling 'ai_visibility_check' by focusing on multi-entity comparison.

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?

Description explicitly mentions use case 'competitive AI-marketing audits' with an example question. It implies that for single-entity checks one should use 'ai_visibility_check', but does not explicitly state when not to use this tool.

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