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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description goes beyond annotations by explaining the probing workflow (per-entity calls to ai_visibility_check), the external API call to api.anthropic.com per probe when _apiKey is provided, and the ranked list output with score, confidence, and signal density. This adds meaningful behavior context.

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 concise and front-loaded with the primary action. It uses a short example in quotes and has no filler. Every sentence contributes to understanding purpose, usage, or output.

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?

Despite not having an output schema, the description enumerates the return fields (score, confidence, signal density) and outlines the internal process. It covers the key behavioral context, including the _apiKey prerequisite and the entity count range (2-8) in the schema. For a tool of this complexity, the description is sufficiently complete.

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 covers 100% of parameters with detailed descriptions. The description adds semantic value by noting that the first entity is treated as the 'subject' for the narrative and the rest as competitors, which is not in the schema. It also explains the relationship between models and _apiKey, and that context is shared across all probes.

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 uses specific verb 'Compare' + resource 'AI presence', states that it probes multiple entities, ranks by score, and surfaces most/least recognized. Provides a concrete example ('does Claude know about us as well as our competitors?'). This clearly distinguishes it from sibling tools like ai_visibility_check (likely single-entity) and compare_entities (generic 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?

Explicitly frames the tool for 'competitive AI-marketing audits' and shows a realistic use case. It also reveals that the tool internally calls ai_visibility_check per entity, implying that ai_visibility_check is for single-entity checks. This gives clear context on when to use this tool vs. alternatives.

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