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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description goes beyond these by disclosing that the tool makes external calls to Anthropic when `_apiKey` is provided, and that the user pays Anthropic directly for those calls. It also names the default model (Workers AI Llama-3.3-70b) and clarifies free vs. paid behavior, adding meaningful cost and external-dependency context.

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. The first sentence states the core purpose, the second covers defaults and key-related behavior, and the third lists concrete use cases. Every sentence earns its place, and the most important information is front-loaded.

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 compensates by clearly stating the return structure: per-model {score, confidence, signals, raw_response} plus a combined view. It also covers default behavior, optional key usage, cost implications, and use cases. For a tool with 4 parameters and external calls, this is sufficiently complete for an agent to select and invoke it correctly.

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?

Input schema description coverage is 100%, so the baseline is 3, and the schema already documents every parameter (entity, models, _apiKey, context) with examples and constraints. The description adds only minor value by restating the default model and cost implication, which is also partially in the schema. It does not introduce new parameter-level meaning, so a 3 is appropriate.

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 about a business / brand / product / topic and score visibility (0-100) per model.' This uses a specific verb ('probe') and resource ('LLMs'), and the scoring output distinguishes it from sibling tools like ask_pipeworx or scan_competitor_ai_presence. The return format and default model further clarify its unique 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?

The description provides explicit use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to pass `_apiKey` (to additionally probe Anthropic) and when the default workers-ai model suffices. However, it does not explicitly name alternatives or state when not to use the tool, so it lacks the full 'when-not/alternatives' guidance found in top-tier examples.

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