Dietary Summarize Survey Distribution

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the bar is lower. The description adds 'governed' and 'without executing a full Monte Carlo simulation,' hinting at deterministic, rule-based behavior, but does not elaborate on data handling, quality checks, or limitations. No contradiction with annotations.

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, front-loaded sentence with no filler. It conveys the core action, inputs, and a key differentiator in an efficient manner.

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 tool's complexity (nested request with dataset and residue profile, plus a detailed output schema), the description is minimal but not fully insufficient. The output schema covers return values, but the description lacks context about governance meaning, prerequisites, or when to choose alternatives, making it only minimally viable.

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%, and the description only mentions 'raw survey dataset,' ignoring the residue_profile component of the request. The input schema itself is rich and self-documenting, but the description fails to compensate for the low coverage or add meaning beyond the schema.

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 uses a specific verb ('Calculate') and identifies the exact outputs ('governed intake distributions, percentiles, and zero-intake prevalence') plus the input source ('raw survey dataset'). It also distinguishes itself from a full Monte Carlo simulation, which differentiates it from sibling tools like dietary_build_probabilistic_intake_summary.

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 phrase 'without executing a full Monte Carlo simulation' provides a clear context for when this tool is appropriate: it is a lighter-weight, governance-based alternative. However, it does not explicitly name an alternative tool or give a direct 'use when...' instruction, 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.