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Household: add to the shopping list

add_shopping_items

Put things on the household's shopping list — only what the person asked for. Something already on the list isn't added twice; something bought recently comes back. Consent: the person's AI setting for groups — 'draft' puts a one-tap card in their Stream (nothing is added until they tap), 'send' adds now, 'off' refuses; a household with someone under 18 is always a draft.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes1–20 things, one per entry, e.g. ["milk", "eggs", "oat yoghurt"].
householdNoThe household's name or householdId from list_households, e.g. "Hemma". Leave out when the person has only one.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
whyNoWhy it's a draft (said to the person).
noteYes
addedNo
itemsYes
itemIdNoThe Stream card, when it's a draft.
statusYes
alreadyNo
householdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior5/5

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

Goes well beyond the annotations: discloses dedup behavior, that recently-bought items are resurrected, and a full three-mode consent model ('draft'/'send'/'off') plus the under-18 always-draft override. This is exactly the behavioral context the annotations (readOnly=false, destructive=false, idempotent=false) cannot carry. The dedup line has mild tension with idempotentHint=false, but the resurrection semantics make the overall behavior genuinely non-idempotent, so it is not a contradiction.

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

Conciseness4/5

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

Front-loads the core action and dedup behavior, then the consent model; every sentence carries load. The semicolon-chained consent sentence is dense but each clause is needed given the branching behavior.

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 no explanation, and the description fully covers the non-obvious behaviors (dedup, resurrection, consent gating, minor-safety override). Nothing an agent needs to invoke this correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so both parameters (items, household) are already fully documented in the schema, including the 1–20 count and the single-household omission rule. The description adds no parameter-level detail beyond that, so the baseline 3 applies.

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 ('Put things') and resource ('the household's shopping list') with a scope qualifier ('only what the person asked for'). An agent can distinguish this from read_shopping_list or check_shopping_items without opening any schema.

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

Usage Guidelines3/5

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

The '— only what the person asked for' clause constrains when to add, and the consent paragraph explains when an add actually takes effect, which is useful. But it never names or routes to alternatives (read_shopping_list, check_shopping_items), so sibling selection 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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