get_forecast_vs_actual

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

No annotations provided, and the description fails to disclose any behavioral traits such as read-only status, authentication needs, or side effects. The description is minimal and does not add context beyond the obvious function.

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 sentence, which is concise, but it lacks structure. It is too brief to provide sufficient information, making it less effective despite being short.

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 no output schema, no annotations, and a minimal description, the tool definition is incomplete. It does not explain return values, interval details, or edge cases, leaving the AI agent with insufficient context to use the tool correctly.

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 coverage is 0%, meaning no descriptions for parameters. The description does not explain the 'intervals' parameter (e.g., format, meaning) or the 'queue' parameter's expected format. It only loosely implies queue is needed.

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 verb 'Return' and the resource 'per-interval forecast vs actual volume, AHT, and staffing for a queue'. It distinguishes from sibling tools like get_available_capacity by focusing on forecast vs actual comparisons.

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?

No guidance on when to use this tool versus alternatives like list_intraday_metrics or get_available_capacity. The description does not mention prerequisites or usage context.

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