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

Adds meaningful behavioral context beyond annotations: default model selection, BYO API key with direct billing to Anthropic, and the per-model return structure. This is exactly the kind of extra context agents need to understand cost and authentication implications.

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?

Three sentences, front-loaded with the core function, then default behavior, return format, and use cases. No redundant wording.

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 probe tool with full schema coverage, the description explains the return shape, cost behavior, and use cases. No output schema is needed because the description covers the key result fields.

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 covers all parameters, but the description adds the concrete default model name ('Workers AI Llama-3.3-70b') and reinforces the dependency between `models` and `_apiKey`, helping the agent reason about configuration.

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 opens with a specific action ('Probe one or more LLMs for what they know...') and clearly defines the output (visibility score 0-100 per model). It distinguishes itself from sibling tools like ask_pipeworx by focusing on visibility scoring rather than Q&A, and mentions concrete use cases.

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?

Provides clear context for when to use ('AI-marketing audits, pre-launch brand checks, competitive monitoring') but does not explicitly mention alternatives or exclusions relative to sibling tools like scan_competitor_ai_presence.

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