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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description goes beyond annotations by disclosing cost implications ('you pay Anthropic directly'), the default model selection, and the return structure (per-model {score, confidence, signals, raw_response} + combined view). This enriches the behavioral picture meaningfully.

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 with no filler. It front-loads the core action and output, then packs additional essential details (default model, cost, output fields) without redundancy. Every sentence contributes useful information.

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?

With no output schema, the description appropriately explains the return format, including per-model fields and the combined view. It covers defaults, optional parameters, and example use cases. For a moderately complex tool with 4 parameters, this is fully complete.

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 description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining the relationship between 'models' and '_apiKey' (passing the key enables Anthropic), the default model when omitted, and the intent behind 'context' ('Helps disambiguate common names'). This cross-parameter guidance is not in the schema alone.

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 action ('Probe one or more LLMs') and the resource (business/brand/product/topic), with a specific measurable outcome ('score visibility (0-100) per model'). It distinguishes itself from sibling tools by focusing on generic visibility scoring across LLMs, not specific entity queries like ask_pipeworx or scan_competitor_ai_presence.

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 AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also provides practical guidance on defaults (Workers AI is free) and when to pass _apiKey (to use Anthropic). However, it does not explicitly state when not to use it or name alternative tools, so it stops short of a 5.

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