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

Annotations already establish read-only, idempotent behavior; the description adds important context about external API calls and billing ('you pay Anthropic directly'), the free default model, and the exact return payload. It stops short of disclosing rate limits or failure modes, but with annotations covering safety, this is substantial additional transparency.

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 dense sentences front-load the core function, then default behavior, then output format and use cases. No redundant phrasing, every sentence contributes new information.

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 the lack of an output schema, the description specifies the per-model result shape and combined view, making the return contract explicit. It also covers default model, optional key material, and applicable scenarios, making the tool fully usable without additional documentation.

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 baseline is 3. The description adds practical meaning: it identifies the free default model, clarifies that _apiKey enables Anthropic and direct billing, and gives representative entity examples. This goes beyond the schema to explain cost and default behavior.

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 opens with a specific verb ('Probe'), names the resource (LLMs), and defines the output (visibility score 0-100 per model). It clarifies the default model and optional Anthropic probe, distinguishing it from sibling data-retrieval tools like ask_pipeworx or recall.

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?

Provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and specifies when to include _apiKey for Anthropic. However, it does not explicitly define when not to use this tool or name alternatives 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.