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

Annotations already declare readOnly/idempotent/non-destructive, so the description adds process detail: it 'probes each entity' with ai_visibility_check and 'returns ranked list with score, confidence, signal density per entity.' This goes beyond the safety profile and explains the aggregation behavior.

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?

Two sentences, no redundancy. The first sentence front-loads the core function, the second provides the use case and expected output. Every sentence serves a purpose without unnecessary elaboration.

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 no output schema, the description explicitly lists the return fields (score, confidence, signal density). The schema covers parameter constraints like 2-8 entities and models. The tool's complexity is adequately addressed for an agent to select and invoke it correctly, though it doesn't mention edge cases like rate limits or cost.

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 schema already provides 100% coverage with descriptions for all 4 parameters, including the first-entity-as-subject convention. The description does not add additional parameter semantics beyond what the schema states, so it earns the baseline score for full schema coverage.

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 a specific action: 'Compare AI visibility across multiple entities side-by-side.' It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the ranking by score and surfacing 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?

Provides a clear use case: 'competitive AI-marketing audits' with an example query. It implies that for a single entity one should use ai_visibility_check, but does not explicitly state exclusions or alternatives. The context is strong enough to guide appropriate use.

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