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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that it internally calls ai_visibility_check for each entity, ranks results, and returns score, confidence, and signal density. This behavioral context goes beyond the annotations without contradiction.

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 and front-loaded with the main action. It is clear but slightly verbose; the second sentence could be tightened. Still, every sentence adds value.

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 composite tool with 4 parameters and no output schema, the description explains the process (probes, ranks, returns), parameter roles, and output structure (ranked list with score, confidence, signal density). It does not mention default model behavior explicitly, but the schema covers that. Overall fairly 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?

The input schema describes all four parameters with descriptions (100% coverage). The description adds significant meaning: it explains that the first entity is treated as the 'subject' for narrative, and the context parameter disambiguates common names. This enhances the schema's information.

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 compares AI visibility across multiple entities, probes each with ai_visibility_check, and ranks them. It provides a concrete use case ('does Claude know about us as well as our competitors?') and distinguishes itself from the sibling ai_visibility_check (single probe) and compare_entities (other comparisons).

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 it is 'useful for competitive AI-marketing audits' and gives an example. While it does not explicitly state when not to use or list alternatives, the purpose and sibling context make the usage clear. A slight deduction for lack of explicit exclusions.

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