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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context beyond those: it discloses cost implications ('BYO key — you pay Anthropic directly'), data flow ('Passed straight through to api.anthropic.com'), and the return structure per-model. It does not mention rate limits or failure modes, but the annotation coverage lowers the bar and the description adds meaningful context, so a 4 is appropriate.

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, well-organized and front-loaded. The first sentence explains the core function and default behavior; the second covers output and use cases. Every word earns its place, with no redundancy or filler. It is compact yet information-dense.

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 read-only, moderately complex tool with 4 params and no output schema, the description is complete. It explains what the tool does, default behavior, optional API key, return format, and use cases. Annotations cover safety and the schema covers param details, so there are no critical gaps. The description is sufficient for an agent to decide when and how to use it.

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 enhances parameter understanding by explaining the default model ('Default model is Workers AI Llama-3.3-70b (free)') and that omitting models uses just workers-ai. It also clarifies _apiKey is only needed if 'anthropic' is in models and that it is passed directly. These details go beyond the schema descriptions, justifying a 4.

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: 'Probe one or more LLMs for what they know... and score visibility (0-100) per model.' It uses a specific verb ('probe') and a distinct resource (LLMs), which differentiates it from sibling tools like ask_pipeworx or scan_competitor_ai_presence. It also specifies the output format and default model, making the purpose unmistakable.

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 use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies when to pass _apiKey and that the default model is free. However, it does not explicitly mention when not to use this tool or name alternative sibling tools, so it falls short of a 5 by not offering exclusions.

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