audio_uncertainty_budget

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention whether the tool is read-only, what it returns (e.g., combined uncertainty, expanded uncertainty, coverage intervals), how it handles invalid inputs, or what the 'standards-claim boundary' implies. This lack of detail makes it difficult for an agent to anticipate the tool's behavior beyond the basic propagation.

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, concise sentence that is front-loaded with the core purpose. It avoids fluff and clearly states the method. However, it is overly sparse for a tool with this complexity; while brevity is good, it sacrifices essential information. Still, as a standalone statement, it is efficient.

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?

The tool has moderate complexity (nested objects, multiple optional fields) and 0% schema coverage. The description provides no information about the return value, how to interpret results, or the criteria for choosing this linear method over Monte Carlo. It also fails to explain the 'standards-claim boundary' concept, leaving the agent without enough context to invoke the tool correctly or choose it appropriately.

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%, and the description does not mention any parameters. The schema defines a complex 'components' array with fields like distribution, sensitivity, coverageFactor, and degreesOfFreedom, but the description adds no meaning about how to populate these fields or what the 'coverageProbability' constraint (0.95) means. The agent must infer entirely from the schema, which is inadequate.

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 states a specific verb ('Propagate'), a precise resource ('calibrated measurement uncertainty'), and the method ('GUM-style linear combination' and 'explicit standards-claim boundary'). This clearly differentiates it from the sibling 'audio_uncertainty_monte_carlo' tool, which implies a Monte Carlo method, so an agent can distinguish them solely from the description.

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 explicit guidance is given on when to use this tool versus the alternative 'audio_uncertainty_monte_carlo'. The description does not state conditions like 'for linear error propagation' or 'when analytical solutions are preferred over simulation', leaving the choice to inference. This is a significant gap given the sibling tools.

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