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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and no destructive behavior. The description adds meaningful context: default model behavior, BYO-key requirement for Anthropic with direct payment, and output structure (per-model details + combined view). This explains traits not captured by annotations.

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 a single, well-structured paragraph of about 3 sentences. It leads with the core action, followed by details on defaults/options, and ends with use cases. Every sentence provides necessary information without redundancy.

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 having no output schema, the description explicitly states the return format (per-model object with score, confidence, signals, raw_response, plus combined view). All four parameters are documented in the schema and described in the description. Annotations cover safety. No gaps remain.

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 coverage is 100%, so the baseline is 3. The description adds value by explaining the default model behavior for the models parameter, clarifying that _apiKey is passed through to Anthropic, and providing context about disambiguation for the context parameter. It also implies that omitting models uses only workers-ai.

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 begins with a specific verb ('Probe') and resource ('LLMs for what they know... and score visibility'), clearly distinguishing the tool's purpose from sibling tools like ask_pipeworx, which are for direct queries. It also lists concrete use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring).

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 context by listing three high-value use cases. However, it does not explicitly state when not to use this tool or name alternative tools (though sibling names provide implicit differentiation). The guidance is strong but stops short of explicit exclusion criteria.

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