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create_ai_meal_plan

Create an AI meal plan from the household's answers (the same answers
the website's guided setup collects) -- requires Subscriber. This only
saves the answers; call generate_ai_meal_plan next to fill it.
`schedule` is day -> meal -> "any", a time limit in minutes ("30"),
"skip" (not eaten, so its calories move to that day's other meals),
"prep_day_N" or "prep_meal_N" (lunch/dinner only). A meal left out is
eaten but not planned, so that day's goals shrink by its share. An
answer that doesn't validate comes back as an error naming the field.

Input Schema

NameRequiredDescriptionDefault
nameYesA name for the plan, shown in the account's plan list.
profileYesThe household's answers. Keys: people, kids_under_8, members (each person's dislikes and likes), diet, cuisines, equipment, schedule, prep_recipes, targets, day_targets, priority, weekly_budget, recipe_source, must_include, notes.
session_tokenNoThe session token check_agent_connection returned for the connected account. Required, unless it's sent as an Authorization: Bearer header on the MCP request instead.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations, the description discloses that this tool only persists answers rather than generating the plan, that it requires Subscriber access, how schedule entries affect calorie allocation, and that invalid answers return field-specific errors. These are meaningful behavioral details for a mutation tool.

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

Conciseness5/5

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

The description is dense but efficient. It front-loads the core action and next-step requirement, then uses the remaining space for behavior that an agent actually needs to know.

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

Completeness4/5

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

Given the complex nested profile object, mutation semantics, and lack of an output schema, the description covers the critical workflow and validation behavior well. It does not explicitly state what successful output to expect, which is a minor remaining gap.

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?

Schema coverage is already 100%, so the baseline is 3. The description adds useful semantics for the nested schedule parameter, including special values like skip, prep_day_N, and time limits, plus the effect of omitted meals; it does not explain every profile key, but it adds significant value beyond the schema.

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?

The description states a specific verb and resource: it creates an AI meal plan from household answers. It also clearly distinguishes this tool from generate_ai_meal_plan by saying this tool only saves the answers while generate_ai_meal_plan fills the plan.

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?

It explicitly instructs the agent to call generate_ai_meal_plan next, which is the key alternative among sibling tools. It also states the Subscriber requirement, giving clear conditions for correct use.

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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