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

Annotations already declare readOnly/idempotent/openWorld, and the description adds valuable behavioral details: the default model is free, using Anthropic requires a BYO key billed directly to the user, and the return payload includes per-model score/confidence/signals/raw_response plus a combined view. It does not contradict annotations and reveals cost and data flow information beyond what structured metadata provides.

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 long, front-loaded with the core action and output, followed by use cases and cost note. Every clause earns its place with no redundancy or filler.

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 tool with 4 parameters and no output schema, the description covers input options, default behavior, output structure (score, confidence, signals, raw_response, combined view), and monetization. It lacks explicit error/edge-case handling, but the provided context is sufficient for an agent to select and invoke the tool correctly in most scenarios.

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 schema already documents all four parameters. The description reinforces the default behavior (omitting models yields workers-ai) and the purpose of context for disambiguation, but it adds minimal new semantics beyond the schema. This matches the baseline for high coverage.

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 a specific verb ('probe') and resource ('one or more LLMs'), and defines the output as a visibility score (0-100) per model. It also distinguishes from siblings like ask_pipeworx (which asks questions) and scan_competitor_ai_presence (focused on competitors) by covering any business/brand/product/topic.

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: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains the default model and when to pass `_apiKey`. However, it does not explicitly call out alternatives or 'when not to use', so it falls 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.