audio_uncertainty_monte_carlo

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

With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It mentions 'deterministic' and the specific sampling style, which hints at reproducibility via a seed, but it does not describe the output format, any side effects, whether the operation is read-only, or any computational or resource implications. Without an output schema, the absence of return-value information is a significant gap.

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 front-loads the core purpose. It is efficient and to the point. However, given the tool's complexity (4 parameters, one being a nested array), a slightly longer description could have added useful detail without becoming verbose. Still, it earns a high score for structure and clarity.

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 Monte Carlo simulation tool with a nested array parameter, no output schema, and no annotations. The description is far too sparse to be complete. It does not explain what the tool returns (e.g., the estimated uncertainty distribution, confidence intervals), what the 'estimate' parameter represents, or how results should be interpreted. Combined with zero parameter descriptions, an agent would struggle to use this 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 description coverage is 0%, meaning parameter descriptions are entirely absent. The tool description does not compensate; it mentions the supported distributions (which map to the 'distribution' parameter) but provides no explanation of 'seed', 'trials', 'estimate', 'sensitivity', 'coverageFactor', or 'degreesOfFreedom'. An agent would have no idea what these parameters mean or how to set them correctly beyond their names and constraints.

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 action ('Propagate'), a specific resource ('input uncertainties'), and the method ('deterministic JCGM-101-style Monte Carlo sampling'). It also names the supported distributions (normal, rectangular, triangular), making the tool's core function unambiguous. Although it does not explicitly enumerate sibling distinctions, the specificity of the method and distributions clearly differentiates it from other uncertainty-related tools like audio_uncertainty_budget.

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 provides no guidance on when to use this tool versus alternatives. It does not mention any selection criteria, prerequisites, or scenarios where this tool is preferred. While the name and description imply it is for Monte Carlo uncertainty propagation, an agent would not know if it should choose this over audio_uncertainty_budget or audio_repeatability_bootstrap without additional context.

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