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

Annotations already declare readOnlyHint and idempotentHint, so the mutation risk is covered. The description adds valuable context: the default model is free, Anthropic requires a BYO key with direct billing, and returns a specific per-model structure (score, confidence, signals, raw_response). This goes beyond the annotation baseline.

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 with no filler. It front-loads the core function, then layers on model specifics and use cases. Every clause adds useful information (default model, BYO key, return shape, audit use cases).

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?

Despite no output schema, the description explains the return type (per-model object + combined view) and the conditional _apiKey requirement. It covers model selection, cost nuance, disambiguation via 'context', and practical applications—sufficient for a read-only, idempotent probe tool with strong annotations.

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 parameter descriptions exist. The tool description adds extra meaning by clarifying the _apiKey billing implication ('you pay Anthropic directly'), noting the free default model, and explaining the models array behavior ('Omit for just workers-ai'). This enriches understanding beyond 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 states a specific action ('Probe one or more LLMs') and a specific outcome ('score visibility (0-100) per model'). It clearly distinguishes itself from siblings like ask_pipeworx or deep_research by focusing on AI visibility auditing with a numeric score.

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 gives concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default model vs. optional Anthropic probing. However, it does not explicitly mention alternatives or when not to use, leaving room for slight ambiguity.

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