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

Annotations already declare readOnly/openWorld/idempotent/non-destructive behavior. The description adds significant context: default model is free Workers AI, Anthropic probing requires a BYO key and direct payment, and the return structure includes score, confidence, signals, and raw_response. This goes beyond the annotation coverage.

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 purpose, then default/cost behavior, then return format and use cases. Every sentence earns its place with no redundancy or fluff.

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?

Even without an output schema, the description reveals the return shape ('per-model {score, confidence, signals, raw_response} + a combined view') and explains all parameter roles and optionality. This is complete for an agent to select and invoke the tool correctly.

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% with all four parameters described. Description adds value by specifying the default model behavior ('Default model is Workers AI Llama-3.3-70b') and clarifying the cost implication of `_apiKey` ('you pay Anthropic directly'), which the schema does not state.

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' with a clear outcome: 'score visibility (0-100) per model'. It distinguishes from siblings like ask_pipeworx and deep_research by focusing on AI knowledge visibility rather than factual Q&A or 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?

Explicitly states use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. Explains when to pass `_apiKey` ('to also probe Anthropic'), giving clear context. It does not name alternative tools, but the guidance is sufficient for most scenarios.

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