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

Annotations already indicate safe, non-destructive, idempotent, and open world behavior. The description adds extra context: it probes each entity with ai_visibility_check, it returns a ranked list with score, confidence, and signal density. No contradictions with annotations; description enhances understanding of how the tool operates internally.

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?

Three sentences with no wasted words: first sentence states main action, second explains the process, third gives a concrete example use case. Perfectly front-loaded and efficient.

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 composite tool with 4 parameters and no output schema, the description covers purpose, usage context, internal behavior (calls ai_visibility_check), parameter semantics (entity order, model choice, context), and output fields (score, confidence, signal density). Minor gap: no mention of error handling or rate limits, but overall highly informative.

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 schema already documents all 4 parameters. The description adds meaningful context beyond schema: entities first entry is treated as subject for narrative, models can be omitted for default workers-ai, and context disambiguates common names. This adds value for correct invocation.

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 it compares AI visibility across multiple entities side-by-side, using ai_visibility_check, then ranks and surfaces most/least recognized. The verb 'compare' and resource 'AI presence across entities' is specific and distinct from sibling tools like ai_visibility_check which only probes a single entity.

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 explicit use case: competitive AI-marketing audits, with an example question. It reveals that it internally calls ai_visibility_check, hinting at the alternative for single entities. However, it does not explicitly state when not to use this tool or list alternative tools for single-entity checks or other comparison methods.

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