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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true. The description adds significant behavioral context: it makes calls to external APIs (Anthropic), details cost implications (BYO key, pay directly), describes the output structure (per-model score/confidence/signals/raw_response + combined view), and explains default model behavior. No contradictions.

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 a single, well-structured paragraph that front-loads the main action, then provides parameter specifics, followed by use cases. Every sentence adds value 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?

Given 4 parameters, no output schema, and annotations, the description covers core functionality, parameter details, output format, and use cases. It briefly mentions 'signals' without elaboration, which is minor. Overall, sufficiently complete for a probe tool.

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 descriptions cover 100% of parameters. The tool description adds extra meaning by explaining parameter purpose, examples (e.g., 'Pipeworx'), and usage tips (supported models, optional context for disambiguation). This provides value beyond the schema alone.

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's purpose: probing LLMs for knowledge about an entity and scoring visibility (0-100) per model. It uses specific verbs ('probe', 'score') and resource ('LLMs'), and distinguishes itself from sibling tools like deep_research or compare_entities.

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 explains default model and requirement for Anthropic API key. However, it lacks explicit when-not-to-use or alternatives among the many sibling tools.

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