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

Annotations already state readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, establishing this as a safe, non-mutating read operation. The description adds value beyond this by revealing that the tool internally calls ai_visibility_check for each entity, ranks the results, and returns specific fields (score, confidence, signal density). This explains the tool's behavior without contradicting 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 three sentences, all informative. It front-loads the core purpose, then explains the mechanism and output, and ends with a concrete use case. Every sentence earns its place, and there is no filler or repetition.

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?

For a multi-entity comparison tool with no output schema, the description provides good context: it links to the sibling tool ai_visibility_check, describes the ranking and return fields, and gives a realistic scenario. It does not cover potential edge cases like entity resolution failures or what happens if an entity is not recognized, but the high schema coverage and clear annotations reduce the need for exhaustive behavioral detail.

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?

The input schema already covers 100% of parameters with descriptions, including the optional models and _apiKey. The description adds meaningful semantics beyond the schema by clarifying that the first entry in entities is treated as the 'subject' for narrative and the rest are competitors. This helps the agent understand how to order the entities array, which is not fully specified in the 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 uses the specific verb 'Compare' with a clear resource ('AI visibility across multiple entities side-by-side'), and distinguishes itself from sibling tools like ai_visibility_check by explicitly mentioning it probes multiple entities and ranks them. It also states the output (ranked list with score, confidence, signal density), making its purpose unmistakable.

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 gives a clear context ('competitive AI-marketing audits') and a user-facing question ('does Claude know about us as well as our competitors?'), indicating when to use the tool. It also implicitly contrasts with single-entity checks by mentioning it probes 'each entity with ai_visibility_check,' but it does not explicitly state when not to use it or name alternative tools for single-entity cases.

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