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

Annotations already cover readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the description doesn't need to repeat safety. It adds valuable context on orchestration (probes each entity with ai_visibility_check, ranks results) and return format (ranked list with score, confidence, signal density), which are not present in annotations.

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: the first states the core purpose, the second explains the probing and ranking mechanism, and the third gives an example use case. Every sentence earns its place, and the illustrative quote adds clarity without bloat.

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 no output schema and four parameters, the description covers the main inputs, the multi-probe orchestration, the ranking output, and a concrete use case. It lacks details on score interpretation or error handling, but these are not essential for an agent to invoke it correctly, especially with annotations covering safety and idempotency.

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 the baseline is 3. The description does not add meaningful extra semantics beyond the schema; the "first entry as subject" detail is already in the entities parameter description. No additional defaults, edge cases, or formatting details are provided.

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 the specific verb "Compare" and identifies the resource as "AI visibility across multiple entities side-by-side." It clearly states the outcome (ranks by score, surfaces most/least recognized) and explicitly names ai_visibility_check as the underlying probe, differentiating it from the single-entity sibling tool.

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 says "Useful for competitive AI-marketing audits" and gives an example, establishing clear when-to-use context. It implies the alternative (ai_visibility_check for single entities) by mentioning it probes each entity with that tool, but it does not explicitly state exclusions or name other comparison 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.