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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds context: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also describes the output structure (ranked list with score, confidence, signal density). No contradiction with 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 long, front-loaded with the core purpose. Each sentence adds value: statement of action, mechanism, use case and output. No wasted words or unnecessary 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?

Despite no output schema, the description explains the return format and includes all necessary context for agent selection. Parameter usage is clarified, tool behavior (calling another tool) is transparent, and the use case is obvious. The tool is moderate complexity; description fully compensates for missing output schema.

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% with all parameters described. The description adds meaning beyond schema: it explains that the first entity is treated as the 'subject' for narrative, and the optional context parameter serves to disambiguate common names. It also clarifies the function of models and _apiKey parameters.

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 starts with a clear verb+resource combination: 'Compare AI visibility across multiple entities side-by-side.' It specifies the action, resource, and scope, and distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (likely different comparison type). The example use case further clarifies purpose.

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 explicitly says 'Useful for competitive AI-marketing audits' and gives an example question, guiding when to use. It also clarifies that the first entity is the 'subject' and rest as competitors. However, it does not explicitly state when not to use (e.g., for a single entity, prefer ai_visibility_check), which would improve clarity.

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