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

Annotations already indicate read-only, idempotent, nondestructive behavior. The description adds valuable context: it returns per-model {score, confidence, signals, raw_response} and a combined view, and notes that passing an API key directly bills the user via Anthropic. No contradictions with 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 three sentences, front-loading the core action and output. Every sentence adds value: what it does, model options, return format, and use cases. No filler.

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 tool with 4 parameters and no output schema, the description covers all necessary ground: what it probes, how scoring works, optional parameters, and return structure. It also includes usage guidance. No gaps.

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 clear descriptions. The description adds context like 'default model is Workers AI Llama-3.3-70b (free)' and explains that _apiKey is passed straight through to Anthropic. This adds practical nuance 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 clearly states the tool probes LLMs for knowledge about a business or topic and returns a visibility score. The verb 'probe' and resource 'LLMs' are specific. It distinguishes from siblings like 'deep_research' or 'scan_competitor_ai_presence' which have different focuses.

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 explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains default vs. paid model usage. However, it does not explicitly state when not to use this tool or list alternative tools for related tasks.

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