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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds significant behavioral context: it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, signal density. This is consistent and enriches understanding beyond the 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 two sentences long, front-loaded with the core purpose, then details behavioral and usage specifics. Every sentence provides value; there is no redundancy or fluff.

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 no output schema, the description clearly states what is returned: a ranked list with score, confidence, and signal density per entity. It covers all parameters and behavioral details (probing, ranking, comparison). The tool is of moderate complexity, and the description is 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?

All 4 parameters are described in the schema (100% coverage). The description adds meaning by explaining that the first entity is treated as the subject, that models defaults to 'workers-ai', that _apiKey is only needed for 'anthropic' model, and that context disambiguates. This assists the agent in parameter selection.

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's purpose: compare AI visibility across multiple entities, probe each with ai_visibility_check, and rank results. It gives a specific use case example ('does Claude know about us as well as our competitors?') and distinguishes from the sibling tool ai_visibility_check, which would handle single entities.

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 context for use ('competitive AI-marketing audits') and a concrete example question. However, it does not explicitly state when not to use the tool or mention alternative tools like compare_entities for other comparison scenarios. The guidance is clear but lacks exclusion criteria.

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