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

The description adds substantial behavioral context beyond annotations: scoring scale (0-100), free vs. paid model access, need for API key, and return structure per model (score, confidence, signals, raw_response) plus combined view. It also discloses cost implications for Anthropic calls. These details complement the readOnly and idempotent annotations without contradiction.

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 compact (4 sentences) and front-loaded with the purpose and score scale. Each sentence contributes: purpose, model selection/cost, return format, and use cases. No redundant or filler content.

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?

Since there is no output schema, the description compensates by clearly stating the return fields (per-model {score, confidence, signals, raw_response} + combined view) and the scoring scale. The tool is moderately complex (4 params, 1 required, optional key), and the description covers purpose, usage, output, and cost, making it complete for effective selection and invocation.

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 baseline is 3. The description adds extra meaning by specifying the default model ('Workers AI Llama-3.3-70b'), clarifying that omitting 'models' yields a free default, and explaining the cost/payment implications of `_apiKey`. These enrich parameter understanding beyond 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' with a clear resource (one or more LLMs) and an explicit outcome (score visibility 0-100). It also names the default model and optional Anthropic integration, making it distinct from sibling tools like ask_pipeworx which focus on asking questions rather than scoring visibility.

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 usage contexts: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also gives guidance on model selection and cost ('Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic'). However, it does not explicitly mention when not to use the tool or name alternative tools, so it stops 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.