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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by disclosing that the default model is free (Workers AI) and that probing Anthropic requires a BYO key with direct payment. It also outlines the return structure (per-model {score, confidence, signals, raw_response} + combined view), which is not in the annotations. No contradiction.

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 long, front-loads the primary purpose, and contains no redundant or irrelevant information. Every sentence serves a clear function: stating the action, detailing configuration, and listing output structure. It is efficient and well-structured.

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 lacking an output schema, the description fully explains the return format (per-model structure + combined view) and covers all parameters with examples. For a tool with 4 parameters and no output schema, this is complete and sufficient for an agent to understand invocation and expected results.

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 description coverage is 100%, so baseline is 3. The description enhances parameter meaning by providing concrete examples for entity (e.g., 'Pipeworx', 'Acme Corp pricing') and clarifying that _apiKey is 'passed straight through to api.anthropic.com'. It also explains the purpose of context ('helps disambiguate common names'). These additions go beyond the schema's basic description.

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 a clear resource 'LLMs', defines the output as a visibility score (0-100) per model, and explicitly lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). This distinguishes it from siblings like scan_competitor_ai_presence by focusing on AI visibility scoring.

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 states when to use the tool: for AI-marketing audits, pre-launch brand checks, and competitive monitoring. It implicitly provides context by mentioning default model and BYO key for Anthropic, but does not explicitly exclude alternatives or compare to sibling tools like scan_competitor_ai_presence, which would warrant a 5.

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