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

The description adds meaningful behavioral context beyond the annotations: it discloses that the tool 'Probes each entity ... with ai_visibility_check, ranks by score', and specifies the output as 'ranked list with score, confidence, signal density per entity'. It also notes that the first entity is treated as the 'subject', which is a non-obvious behavior. Annotations already declare readOnly/idempotent, so the bar is lower; this extra detail earns a 4.

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, then mechanism and use case. Every sentence contributes: it states what it does, how it does it, and when to use it. There is no redundant filler or repetition of schema details.

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?

The tool is moderately complex (4 params, no output schema), but the description compensates by summarizing the return format ('ranked list with score, confidence, signal density per entity') and the procedural behavior. It also hints at the dependency on ai_visibility_check. Without an output schema, this description is nearly sufficient for an agent to anticipate what the tool will return. A minor gap is lack of detail on error conditions or model selection behavior, but overall it is 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?

Schema description coverage is 100%, and the description itself does not add parameter-level details beyond what the schema provides. The schema already documents that entities is an array of 2-8 with the first as subject, and explains models, _apiKey, and context. Per the baseline rule, when schema covers parameters fully, a score of 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 starts with a specific verb+resource: 'Compare AI visibility across multiple entities side-by-side.' It clearly distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the probing mechanism (ai_visibility_check) and the ranking output. The competitive audit use case is explicit, making the tool's purpose unambiguous.

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 provides a clear context: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"' This tells the agent when to use the tool, but it does not explicitly state when not to use it or mention alternatives. It lacks the 'when-not' guidance that would earn a 5, but the context is sufficiently clear.

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