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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral details: it calls ai_visibility_check for each entity, ranks results, and returns a list with score, confidence, and signal density. This goes beyond annotations by explaining the operational flow and output format.

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 at 4 sentences. It front-loads the primary action and then efficiently adds details on process, use case, and output. Every sentence contributes unique information without redundancy.

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?

Given the tool's moderate complexity (4 parameters, no output schema), the description covers the core functionality, output format, and relationship to sibling tools. It lacks details on error handling or edge cases, but for a simple comparison tool, it is sufficiently complete. Annotations already cover safety aspects.

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 all 4 parameters (100% coverage), so baseline is 3. The description adds value by explaining that the first entity in entities is treated as the 'subject' for narrative and that omitting models defaults to workers-ai. This provides semantic hints not present in the schema.

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 verb 'Compare' and the resource 'AI visibility across multiple entities'. It specifies the process: probes each entity with ai_visibility_check, ranks by score, and surfaces the most/least recognized. The usage example further clarifies the purpose, distinguishing it from the sibling ai_visibility_check tool which handles single entities.

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 clear context for when to use the tool: 'Useful for competitive AI-marketing audits' and gives a concrete example. It implies that this tool is for comparing multiple entities, while the sibling ai_visibility_check is for single entities. However, it does not explicitly state when not to use this tool or list alternative tools for specific scenarios.

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