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Read daily targets

get_targets
Read-only

Read the person's daily calorie and macro targets and unit system before planning meals or judging a day, unless already read in this conversation. Each period applies from startsOn until the next; values null means no targets from that day, so judge a past day by its own period, and by list_days, since a partly logged day's totals are only a lower bound. Targets are always kilocalories and grams. units (metric, imperial for US customary, or uk; null when unknown) is how to talk to the person about portions and weights; meal items still use each food's own units. Diet, allergies, likes and dislikes are not saved: use what the person says in the conversation, treat stated allergies as constraints, and never infer allergy safety from missing product data.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
unitsYes
periodsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover the read-only/safe profile, and the description adds real behavior the schema cannot: period semantics ('applies from startsOn until the next'), null meaning 'no targets from that day', null units meaning unknown, and the caveat that a partly logged day's totals are only a lower bound. It also warns that diet/allergy data is not persisted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose and read-once rule are correctly front-loaded, but the definition is a dense run-on that mixes target semantics, unit semantics, period logic, and an allergy-safety policy into a single block. The closing allergy guidance is valuable but marginally off-scope for a read tool and dilutes the core instruction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be re-specified, yet the description still supplies the interpretation rules an agent needs (null = no targets, per-period judgement, lower-bound caveat). Nothing required to call or correctly interpret this tool is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4. The description instead documents the semantics of the values returned (null handling, period boundaries, unit encodings), which is useful but sits outside parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource set: 'Read the person's daily calorie and macro targets and unit system'. It is immediately distinguishable from set_targets, get_profile, and list_days, and it pins down the returned units (kilocalories and grams).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit trigger ('before planning meals or judging a day'), an exclusion ('unless already read in this conversation'), and names the sibling needed for the adjacent task ('by list_days'). Nothing about when to call it is left to inference.

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

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