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

Annotations already declare read-only, open-world, idempotent, and non-destructive, so the safety profile is covered. The description adds valuable behavioral context: it probes each entity with ai_visibility_check, ranks entities, and returns a ranked list with score, confidence, and signal density. It also discloses that the first entity is treated as the 'subject' and the rest as competitors. This goes beyond the annotations without contradicting them.

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 compact yet information-dense: three sentences cover purpose, mechanism, use case, and return format. There is no filler or repetition of schema data. It is front-loaded with the main action ('Compare AI visibility') and every sentence earns its place.

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, no output schema, and strong annotations, the description is fairly complete. It explains the process (probes each entity), the ranking output, and the practical use case. It does not mention potential costs or runtime implications of probing multiple entities across models, which would be useful in an open-world context, but this is not a critical gap given the other signals.

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%, so the baseline is 3. The description does not add significant meaning beyond the schema; it mentions entities, models, _apiKey, and context only implicitly. The schema already explains that entities are 2-8 names, the first is the 'subject', models are optional with 'anthropic' requiring _apiKey, and context disambiguates. The description adds no new parameter semantics, so a 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 clearly states the tool's function: compare AI visibility across multiple entities side-by-side, probe with ai_visibility_check, rank by score, and surface most/least recognized. It distinguishes itself from single-entity tools like ai_visibility_check and provides a concrete use case (competitive AI-marketing audits). The verb 'Compare' and specific resource 'AI presence' make the 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 explicitly mentions when to use the tool ('competitive AI-marketing audits') and gives a representative example question. It also references the underlying probe tool (ai_visibility_check), implying an alternative for single-entity checks. However, it does not explicitly state 'when not to use' or contrast with sibling tools like compare_entities or deep_research, so it's slightly below a perfect 5.

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