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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds valuable behavioral context: weekly re-measurement ('same call returns the same figures until the next release'), error behavior for unknown slugs (lists all valid slugs), the distinction between absent and measured-zero, and licensing/auth details (CC BY 4.0, no key, no rate limit). This greatly exceeds what annotations alone provide.

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 front-loaded with purpose, then systematically covers usage, error semantics, refresh cadence, and licensing. Every sentence adds information; there is no fluff or repetition. It is long but dense and well-organized.

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 the tool's moderate complexity, the description covers all needed context: when to use, how to get the slug, error handling, data freshness, absence semantics, licensing, and alternative tools. It is comprehensive for a single-parameter lookup tool with good annotations and an output schema.

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?

The schema already covers the slug parameter well (pattern, format examples, 'Not the display name'). The description reinforces this and adds practical guidance: 'call list_tracked_brands first if you are unsure' and 'read the slug from get_visibility_index.' Since schema coverage is 100%, the baseline is 3, and the extra usage context earns a 4.

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 precise statement of what the tool returns: 'share of answer per engine, rank, quadrant, how many panel prompts name it, and which ones.' It names the specific resource (one brand's standing in the current DABYTE release) and immediately differentiates from siblings by directing to get_visibility_index for the whole field and get_history for temporal trends.

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 explicitly states when to use this tool: 'Use this when a specific brand is named.' It also gives concrete alternatives: 'For the field as a whole use get_visibility_index; for this brand over time, get_history.' It even advises calling list_tracked_brands first if uncertain about the slug, showing clear decision guidance.

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