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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds context that the tool internally calls ai_visibility_check for each entity, returns scores, confidence, and signal density, and requires an Anthropic API key if probing with that model. No contradictions with annotations, and the additional behavioral detail is valuable beyond structured 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 two sentences totaling ~50 words, with no filler. It front-loads the primary action, then details the process and output. Every sentence adds value: purpose, mechanism, use case, and return format.

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 tool has 4 parameters and no output schema; the description explains the output format (ranked list with score, confidence, signal density). It does not mention the 2-8 entity constraint (already in schema) or error handling. However, given the annotations and schema richness, the description is sufficiently complete for an AI agent to understand how to invoke the tool.

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% with descriptions for all 4 parameters. The description adds extra meaning beyond the schema: it explains that the first entry in 'entities' is treated as the 'subject' for narrative purposes, and clarifies that 'models' can be omitted for just workers-ai. This enhances understanding without repeating schema content.

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, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides a concrete use case example ('does Claude know about us as well as our competitors?') and distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (generic 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 explicitly says the tool is useful for competitive AI-marketing audits and gives a sample question. It implies when to use (comparing multiple entities) but does not explicitly state when not to use it or list alternative tools for single-entity checks. The context is clear but lacks explicit exclusions.

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