assess_intake_vs_target

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

With no annotations, the description carries the burden of behavioral disclosure. It explains the computation purpose, required inputs, and optional adjustments, adding value beyond the schema. It does not disclose edge cases or explicit read-only behavior, but for a calculation/assessment tool, the behavior is sufficiently transparent.

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 concise, with two sentences. The first sentence states the core purpose and outputs; the second adds key input requirements and options. No superfluous words, and it is well-structured for quick comprehension.

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 (11 parameters, nested 'diet' object, output schema), the description is somewhat incomplete. It provides essential input hints and links to calc_prnt_targets, but it does not explain roles of most parameters or the interaction with sibling tools. The output schema likely covers return values, but parameter context is lacking, making it minimally viable rather than complete.

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 has 0% description coverage, so the description must compensate for parameter meaning. It explains the 'diet' subfields (avg_energy_kcal/avg_protein_g) and two optional parameters (is_edema, height_cm, pd_glucose_kcal_per_day) but leaves many required and optional parameters (e.g., age_years, sex, weight_kg, ckd_stage, dialysis_mode, growth_status, vegetarian_mode) unexplained, despite their importance in PRNT target assessment. The partial coverage is insufficient.

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 tool's function: '对照 PRNT 目标评估 3 日饮食日记均值' (assess 3-day diet diary mean against PRNT targets) and specifies outputs like energy/protein achievement rate, gap, and PEW risk. It uses a specific verb and resource, and the mention of '与 calc_prnt_targets 一致' distinguishes it from the sibling tool calc_prnt_targets.

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 implies usage when a 3-day diet diary with averaged values is available and PRNT targets are needed. It states required diet fields and lists optional adjustments (edema correction, PD glucose deduction), providing clear context. However, it does not explicitly exclude scenarios or mention alternatives like assess_pew_risk for detailed PEW assessment, so it lacks explicit when-to-use/alternatives.

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