Compare Intake To Dri

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

No annotations are supplied, so the description carries the full burden, and it does substantial work: it discloses exactly which comparisons are computed (EAR %, RDA/AI %, UL assessment, AMDR compliance, risk classification). What it omits is the operational frame an agent needs with zero annotation coverage — whether the call is read-only/no-side-effect and whether DRI tables or a session must already exist (several sibling tools manage DRI table/session state).

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?

Purpose is front-loaded in the first sentence and the supporting blocks are bulleted and scannable. The 'Comparison metrics calculated' list is largely a restatement of return values already covered by the output schema, and the five-item use-case list is longer than needed, so some trimming is possible.

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?

With an output schema present, the description need not explain return values, yet it spends most of its length on them. The genuine gaps for a computation tool of this complexity — required units for intake_data, whether a DRI session/table prerequisite exists, and whether the call mutates anything — go unaddressed.

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 100%, so the nested input_data fields (age_range, gender, intake_data, comparison_type) are already documented in structured form. The description adds no meaning beyond that — notably it never states the expected units for intake_data values, which is the one semantic the schema itself leaves ambiguous.

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 names a specific verb and resource ('Compare actual macronutrient intake against DRI recommendations') and enumerates the comparison metrics, so the operation is unambiguous. It does not, however, distinguish itself from adjacent DRI tools (get_dri_lookup_from_session, calculate_dri_from_eer, get_specific_macronutrient_dri), so sibling differentiation is left to inference. There is also a minor tension between the 'macronutrient' framing and the EAR/RDA/UL metrics, which are nutrient-general.

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?

An explicit 'Use this tool when:' block supplies five concrete contexts (nutrition assessment, counseling, adequacy evaluation, improvement plans, research), which is clear positive guidance. It stops short of exclusions or naming the alternative DRI tools to use for lookup-only or EER-based needs, and the listed scenarios are somewhat generic rather than distinguishing conditions.

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