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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds valuable context about the free Workers AI model, the cost implication of providing an Anthropic API key ('you pay Anthropic directly'), and the per-model return structure. This goes beyond annotation-provided safety info, earning a 4.

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: opening states the core function, middle explains default and paid options, closing lists use cases and return shape. Every sentence carries distinct information with no filler, making it both concise and well-structured.

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 discloses the return format ('per-model {score, confidence, signals, raw_response} + a combined view'). It covers cost, defaults, and use cases. Minor gap: no mention of potential rate limits or failure modes, but overall it is sufficiently complete for a read-only probe tool with 4 simple parameters.

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% (all 4 parameters documented), so baseline is 3. The description adds extra meaning by explaining that _apiKey is 'Passed straight through to api.anthropic.com' and that 'context' helps disambiguate common names. It also clarifies the relationship between models and _apiKey, which is not fully explicit in 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 uses a specific verb ('Probe') and resource ('LLMs') and states the measurable outcome ('score visibility (0-100) per model'). It clearly distinguishes itself from sibling tools like ask_pipeworx and deep_research by focusing on AI visibility scoring rather than answering questions or doing general research.

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 clear use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to use the free default versus paid Anthropic probing. However, it does not explicitly state when not to use the tool or mention alternatives, 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.