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

The description goes beyond annotations by detailing the probing mechanism, ranking by score, and return format (ranked list with score, confidence, signal density). It also mentions the first entity is treated as 'subject', adding context that is not in annotations or schema. No contradiction with readOnly/openWorld/idempotent hints.

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 four concise sentences, each adding value: purpose, mechanism, use case, and output. No redundant phrases or repeated 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 description is complete for a tool with no output schema: it states the return format, use case, and how the tool works. Combined with strong annotations (readOnly, openWorld, idempotent) and full schema parameter descriptions, the context is fully covered.

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 coverage is 100%, so baseline is 3. The description adds meaningful parameter semantics by explaining the first entity is the 'subject' and the rest are competitors, which is not in the schema. It also gives a concrete example of context usage, though it doesn't elaborate on models or _apiKey beyond what the schema already states.

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, with specific verbs like 'Compare' and 'Probes'. It distinguishes itself from sibling ai_visibility_check by explicitly mentioning it probes each entity with that tool, clarifying it is a multi-entity comparison.

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 use case ('competitive AI-marketing audits') and example query, implying when to use it. It references ai_visibility_check as the probe, hinting at the alternative for single-entity checks, but does not explicitly state exclusions or name other alternatives like compare_entities.

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