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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context: it performs N+1 probes, calls ai_visibility_check internally, ranks results, and returns score/confidence/signal density. It also notes that _apiKey is passed to api.anthropic.com per probe, covering auth behavior succinctly.

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 concise and well-structured: purpose first, then mechanics, then use case, then output details. Every sentence adds value with no filler, and the front-loading makes the tool's core function immediately clear.

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 tool with 4 parameters and no output schema, the description covers input semantics (entities, models, context, API key), output format (ranked list with score/confidence/density), use case, and the relationship to ai_visibility_check. This is fully complete for an agent to select and invoke correctly.

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 descriptions cover 100% of parameters, earning baseline 3. The description adds meaning beyond the schema by explaining that the first entity is treated as the 'subject' for narrative and the rest are competitors, and clarifies the shared 'context' disambiguates names. This extra context justifies a 4.

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 AI visibility') and resource ('multiple entities side-by-side'), clearly distinguishing it from the sibling ai_visibility_check (single entity) and compare_entities. It also states the ranking/recognition outcome, making the tool's function unmistakable.

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') and an illustrative question ('does Claude know about us as well as our competitors?'). It implicitly contrasts with ai_visibility_check by saying it probes each entity with that tool, but does not explicitly state when not to use it or name alternative tools like compare_entities.

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