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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds that the tool probes each entity with ai_visibility_check and returns a ranked list with score, confidence, signal density. No contradiction with annotations. The description complements annotations well.

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 a parenthetical example. The main verb and purpose are front-loaded. Every sentence adds value; no superfluous words.

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?

Given the tool has 4 parameters (all documented), annotations covering safety, and no output schema, the description explains the process, entity ordering, and output shape. It is mostly complete but could briefly mention that the entities array limit (2-8) is enforced, though that's in the 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 description coverage is 100% (all parameters have descriptions). The description adds semantic value by stating that the first entity in the array is treated as the 'subject' for narrative and the rest as competitors. This provides usage-context beyond schema details.

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 side-by-side, using ai_visibility_check internally, and ranks them. It distinguishes from sibling tool ai_visibility_check (single entity) and compare_entities (general comparison) by specifying the use case for competitive AI-marketing audits.

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, indicating when to use. It does not explicitly state when not to use, but the context implies using ai_visibility_check for single entity checks. A brief exclusion statement would improve it.

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