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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description doesn't need to restate those. It adds valuable process detail: probes each entity with ai_visibility_check, ranks results, and describes the return format (ranked list with score, confidence, signal density). This goes beyond annotations without contradicting them.

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, front-loaded with the primary action, and every sentence earns its place. It includes a concrete example and return format without fluff, making it highly efficient.

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 read-only comparative tool with 4 parameters and no output schema, the description is complete: it explains the process, the output structure, and a concrete use case. The schema handles parameter constraints (2-8 entities, optional models), and annotations cover safety. No meaningful gaps remain.

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%, so the schema already documents all parameters. The description adds minimal extra meaning—mainly that context is applied to every probe and that ai_visibility_check is used for each entity. It doesn't elaborate on the 'models' or '_apiKey' parameters beyond what the schema provides, so baseline 3 is appropriate.

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 a specific verb ('Compare') and identifies the resource ('AI visibility across multiple entities'), clearly distinguishing it from the sibling ai_visibility_check which handles single entities. It states exactly what it does: probes each entity, ranks by score, and surfaces the most/least recognized.

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?

It provides a clear use case ('competitive AI-marketing audits') with an example query. While it doesn't explicitly name alternatives or exclusions, the comparison to ai_visibility_check is implied and the context is sufficient for an agent to decide when 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.