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IZeat

Update profile

update_profile
DestructiveIdempotent

Stores the customer's name, a new daily target, the time zone their days are counted in, and/or onboarding progress. The only way anything about the profile or onboarding state changes. Marked destructive because a new name or time zone replaces the previous one; targets are never overwritten, a new one starts from the day given. Profile fields are optional; only what is passed changes. A target is never overwritten: passing one adds a new target valid from today in the account's time zone when target.valid_from is omitted, otherwise from the day given (the customer's word, or YYYY-MM-DD only when they said the full date), and earlier days keep the target they had. At least one of kcal, protein_g, carb_g, fat_g is required in a target; each is an integer, grams for macros. Set only what the customer actually chose: never fill in a nutrient they did not mention and never calculate a target for them. onboarding_status is in_progress while setup continues, skipped when the customer chooses later, and completed only after first name, last name and a target exist. timezone is an IANA name such as Europe/Paris or Africa/Johannesburg, derived from the city the customer names; pass it when get_connected_account reports no time zone or when they say they moved. idempotency_key is generated once for this profile change and reused only if that exact call must be retried. Returns the profile and the target now in force. Example: update_profile { "first_name": "Seb", "target": { "valid_from": "2026-09-03", "kcal": 2000, "protein_g": 150 }, "idempotency_key": "profile-a3f9c1" } → relay the receipt: name stored, target 2000 kcal · P 150g from 2026-09-03. statement is the customer-facing IZeat message. It begins with "IZeat ·" so customers can distinguish information returned by IZeat from the assistant's own response. For write operations, it also states whether the requested change was recorded in their IZeat account. Account: Sam (review@izeat.app)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNo
timezoneNoIANA time zone the customer's days are counted in, e.g. Europe/Paris
last_nameNo
first_nameNo
idempotency_keyYesRandom per profile change; reuse only when retrying this exact call
onboarding_statusNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
dataYes
statementYesCustomer-visible IZeat receipt. Its value begins with "IZeat ·".

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 declare destructive/idempotent, and the description goes well beyond them: what is destroyed ('a new name or time zone replaces the previous one'), the additive target model ('targets are never overwritten, a new one starts from the day given'), and onboarding lifecycle rules. It also explains idempotency_key generation and reuse, which annotations only hint at.

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?

Purpose and the destructive/additive distinction are front-loaded, then constraints follow in a logical order, and a concrete example anchors it. It is dense and long, and the closing 'statement is the customer-facing IZeat message' paragraph is somewhat tangential given an output schema already exists, but each block still carries real 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?

For a 6-parameter mutation with a nested target object and one enum, the description covers mutation semantics, idempotency, enum state transitions, formatting rules and an example. With an output schema present it need not re-explain return values, and it does not.

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

Parameters5/5

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

Schema coverage is only 33%, so the description carries the load and does: it specifies the target nutrient constraint (at least one of kcal/protein_g/carb_g/fat_g required, integers, grams), the omit-for-today vs YYYY-MM-DD semantics of valid_from, and the IANA format for timezone. It also warns against inventing values ('never fill in a nutrient they did not mention').

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 ('Stores the customer's name, a new daily target, the time zone... and/or onboarding progress') and then draws a hard boundary against siblings: 'The only way anything about the profile or onboarding state changes.' An agent can tell this apart from get_profile without opening either schema.

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

Usage Guidelines4/5

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

Gives concrete trigger conditions, e.g. pass timezone 'when get_connected_account reports no time zone or when they say they moved', and enumerates when each onboarding_status value applies. It names a sibling as a trigger but never states the read-side alternative (get_profile) for inspecting state, so routing is clear but not fully closed.

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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