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

With no annotations, the description carries the transparency burden. It discloses that the tool returns screens, total count, online_count, and real-time audience data when available, including a caveat about data availability. This conveys the read-only nature and likely output shape, though it does not discuss potential side effects or limitations beyond data availability.

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 well-structured with clear sections: summary, when to use, returns, and example. It is somewhat long due to the detailed RETURNS list, but every section contributes information useful for tool selection and invocation. The main purpose is front-loaded.

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 four nested parameters and no output schema, the description sufficiently covers the tool's behavior, return structure, and usage scenarios. It does not mention error handling or rate limits, but for a discovery query tool, the provided details are strong. The example further enhances completeness.

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 100% of parameters, so the baseline is 3. The description adds value with a concrete example showing how to combine venue_types, location, audience_profile, and limit, plus a RETURNS section that clarifies how parameters affect the response. This goes beyond the schema's per-field descriptions.

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 function: discovering available DOOH screens across the exchange network, with specific use cases for filtering by venue, location, and audience. It does not explicitly differentiate from siblings like get_live_audience or semantic_audience_search, but the verb 'discover' and resource are specific enough to distinguish it.

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 'WHEN TO USE' section explicitly lists three key scenarios: finding screens by venue type, location, or audience profile, plus getting an overview. This gives clear context for when to use the tool, but it does not state when not to use it or mention alternatives like get_live_audience, so it stops short of a 5.

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