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

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses cost implications ('you pay Anthropic directly'), default model choice, and return structure (per-model fields + combined view). No contradiction 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?

A single paragraph of 4 efficient sentences, front-loaded with the core action and outcome. Every sentence adds necessary context without redundancy or 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?

Despite no output schema, the description covers return structure. Parameters are fully explained. Use cases and pricing are included. The tool is simple (4 params, 1 required), and the description provides sufficient context for an AI agent to invoke 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?

All 4 parameters have schema descriptions (100% coverage). The description adds value by explaining the default model behavior, how apiKey is used (passthrough), and that 'models' can be omitted. This goes beyond the schema to clarify invocation logic.

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 a specific verb ('probe'), resource ('LLMs'), and outcome ('score visibility 0-100'), making the tool's purpose unambiguous. It also lists use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') that differentiate it from siblings like 'deep_research' or 'ask_pipeworx'.

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 explains when to use the optional apiKey ('if anthropic is in models') and highlights the default free model. It implies usage contexts (marketing audits) but does not explicitly state when NOT to use this tool or compare with siblings 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.