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

With no annotations, the description carries full burden. It discloses data freshness (refreshed every 10 seconds), the data source (edge AI sensors), and explains return fields including a staleness metric. It lacks explicit rate limits or error behavior but effectively communicates the real-time nature.

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 long but well-structured with WHEN TO USE, RETURNS, and EXAMPLE sections. Each part adds value; the main verb is front-loaded. A slight deduction for verbosity in the returns list, though it's necessary given the lack of an output schema.

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?

This is a simple one-parameter tool with no output schema. The description comprehensively covers what the tool does, when to use it, the return fields (with meanings), and an example call. No significant gaps remain.

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 covers 100% of parameter documentation for screen_id. The description adds a concrete example ID, but no additional semantic detail beyond what the schema already states. Baseline 3 is appropriate.

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 'Get real-time audience data for a specific screen,' a specific verb+resource+scope statement. It clearly distinguishes from sibling tools like get_audience_forecast or get_analytics by emphasizing 'real-time' and 'specific screen.'

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 concrete use cases: pre-buy checks, live campaign monitoring, and detailed audience signals. It stops short of naming alternatives or exclusions, so it doesn't earn a 5, but the context is clear enough for an agent to decide.

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