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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses that it 'probes each entity' using another tool, 'ranks by score', and returns a 'ranked list with score, confidence, signal density per entity'. This adds useful behavioral context about the internal process 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 three well-structured sentences: function, method, use case, and return format. Every sentence earns its place with no redundancy or filler, making it easy to parse quickly.

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, the description appropriately explains the return format (ranked list with score, confidence, signal density) and the use case, making it self-contained. The annotations cover safety characteristics, and the schema covers parameters, so nothing critical is missing.

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?

The schema already documents all 4 parameters with 100% coverage, so the baseline is 3. The description adds only marginal parameter context by mentioning 'your brand + N competitors' which aligns with the 'entities' schema description, but doesn't introduce new details about models, _apiKey, or context beyond what the schema provides.

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 with a specific verb ('Compare AI visibility across multiple entities side-by-side') and explicitly distinguishes it from single-entity checks by referencing 'ai_visibility_check' and multi-entity ranking. The example query ('does Claude know about us as well as our competitors?') further anchors its purpose.

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?

It identifies the appropriate context ('competitive AI-marketing audits') and implies when to use it (comparing multiple entities) versus single-entity tools like ai_visibility_check. While it doesn't explicitly name alternative tools or state when not to use it, the multi-entity framing provides clear guidance.

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