whatevr
Log meals and read calories and macros from any AI assistant. Pairs with the whatevr iPhone app.
Sollte ich dies verwenden
Qualität und Sicherheit
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"whatevr": {
"url": "https://api.trywhatevr.app/mcp"
}
}
}Remote-Endpunkte
https://api.trywhatevr.app/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (12)
🟡log_anything(text, logged_at, tz_offset_min)
Log food, drinks, water, exercise, or a weigh-in from one natural-language sentence — the same parser as the app's voice logging. Examples: 'two eggs and toast', '30 minute run', '3 glasses of water', 'weighed 184 this morning'. Spoken dates like 'yesterday I had pizza' work. Describe the FOOD, not just numbers: stating explicit nutrition in the text ('burrito, 800 calories') pins those totals exactly but logs ONE pre-computed item with no ingredient breakdown — if you do that, follow up with update_log's `ingredients` array so the log keeps its per-item detail.
Eingabe-Schema
{
"type": "object",
"properties": {
"text": {
"type": "string",
"maxLength": 2000,
"description": "What to log, in plain words."
},
"logged_at": {
"type": "string",
"description": "Bare-local timestamp YYYY-MM-DDThh:mm:ss (no zone). Omit to use the user's 'now'."
},
"tz_offset_min": {
"type": "number",
"description": "The user's JS getTimezoneOffset() — minutes BEHIND UTC (e.g. 240 for EDT). Optional: when omitted, the user's app-reported timezone is used (UTC only if the account has never reported one)."
}
},
"required": [
"text"
]
}⚪log_exercise(activity, duration_minutes, calories_burned, logged_at, tz_offset_min)
Log structured exercise with a known calorie burn. For an ESTIMATED burn from plain text, use log_anything ('30 minute run').
Eingabe-Schema
{
"type": "object",
"properties": {
"activity": {
"type": "string",
"description": "e.g. 'running'"
},
"duration_minutes": {
"type": "number",
"minimum": 1,
"maximum": 1440
},
"calories_burned": {
"type": "number",
"minimum": 0,
"maximum": 20000
},
"logged_at": {
"type": "string",
"description": "Bare-local timestamp YYYY-MM-DDThh:mm:ss (no zone). Omit to use the user's 'now'."
},
"tz_offset_min": {
"type": "number",
"description": "The user's JS getTimezoneOffset() — minutes BEHIND UTC (e.g. 240 for EDT). Optional: when omitted, the user's app-reported timezone is used (UTC only if the account has never reported one)."
}
},
"required": [
"activity",
"duration_minutes",
"calories_burned"
]
}⚪log_weight(weight_lb, note, logged_at, tz_offset_min)
Log a weigh-in in pounds. The newest reading also becomes the user's current weight (drives their calorie targets).
Eingabe-Schema
{
"type": "object",
"properties": {
"weight_lb": {
"type": "number",
"minimum": 20,
"maximum": 1500
},
"note": {
"type": "string"
},
"logged_at": {
"type": "string",
"description": "Bare-local timestamp YYYY-MM-DDThh:mm:ss (no zone). Omit to use the user's 'now'."
},
"tz_offset_min": {
"type": "number",
"description": "The user's JS getTimezoneOffset() — minutes BEHIND UTC (e.g. 240 for EDT). Optional: when omitted, the user's app-reported timezone is used (UTC only if the account has never reported one)."
}
},
"required": [
"weight_lb"
]
}⚪log_water(glasses, amount_fl_oz, logged_at, tz_offset_min)
Log water — glasses (8 fl oz each) or an exact amount_fl_oz. Defaults to 1 glass.
Eingabe-Schema
{
"type": "object",
"properties": {
"glasses": {
"type": "number",
"minimum": 1,
"maximum": 32,
"description": "8 fl oz each; used when amount_fl_oz is absent"
},
"amount_fl_oz": {
"type": "number",
"minimum": 1,
"maximum": 256
},
"logged_at": {
"type": "string",
"description": "Bare-local timestamp YYYY-MM-DDThh:mm:ss (no zone). Omit to use the user's 'now'."
},
"tz_offset_min": {
"type": "number",
"description": "The user's JS getTimezoneOffset() — minutes BEHIND UTC (e.g. 240 for EDT). Optional: when omitted, the user's app-reported timezone is used (UTC only if the account has never reported one)."
}
}
}🟢get_today(tz_offset_min)
Today's complete picture: calorie/macro totals vs targets, water, streak, exercises, and the item-by-item food log (`recent`: every logged item's name, calories, macros, ingredients). Each `recent` item's `id` works with update_log to correct mistakes.
Eingabe-Schema
{
"type": "object",
"properties": {
"tz_offset_min": {
"type": "number",
"description": "The user's JS getTimezoneOffset() — minutes BEHIND UTC (e.g. 240 for EDT). Optional: when omitted, the user's app-reported timezone is used (UTC only if the account has never reported one)."
}
}
}🟢get_day(date)
Everything logged on a specific day, INCLUDING each food item's name and nutrition (same shape as get_today). Use this to answer “what did I eat yesterday / on <date>”. Each `recent` item's `id` works with update_log to correct mistakes.
Eingabe-Schema
{
"type": "object",
"properties": {
"date": {
"type": "string",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$",
"description": "YYYY-MM-DD"
}
},
"required": [
"date"
]
}🟢get_progress(days, anchor, tz_offset_min)
Daily calorie/macro/exercise TOTALS over time + the weight trend (no food item names — use get_day for what was actually eaten).
Eingabe-Schema
{
"type": "object",
"properties": {
"days": {
"type": "number",
"description": "1–730, or 0 for all-time (default 30)"
},
"anchor": {
"type": "string",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$",
"description": "Window end date (default: the user's today)"
},
"tz_offset_min": {
"type": "number",
"description": "The user's JS getTimezoneOffset() — minutes BEHIND UTC (e.g. 240 for EDT). Optional: when omitted, the user's app-reported timezone is used (UTC only if the account has never reported one)."
}
}
}🟢get_me
Who the user is: name/handle/email, health profile, calorie/macro targets, and derived metrics.
Eingabe-Schema
{
"type": "object",
"properties": {}
}🔴update_goals(goal, goal_weight_lb, rate_lb_per_week, activity_level, calorie_target, ...)
Change the user's goal and daily targets — the same settings as the app's Goals screen. Send ONLY the fields to change. Each target takes a number (a custom target) or "auto" (computed from their body, activity and goal). The server keeps calories and macros consistent (protein×4 + carbs×4 + fat×9 ≈ calories), so the targets it returns can differ slightly from what you sent — report the returned ones. Current weight is not here: log a weigh-in with log_weight. Needs the user's "Change goals & settings" switch for this connection (whatevr app → Settings → Connections); if it's off you'll get a message saying so — relay it. The user is notified of every change.
Eingabe-Schema
{
"type": "object",
"properties": {
"goal": {
"type": "string",
"enum": [
"lose",
"maintain",
"gain"
]
},
"goal_weight_lb": {
"type": "number",
"minimum": 50,
"maximum": 700
},
"rate_lb_per_week": {
"type": "number",
"minimum": 0,
"maximum": 2,
"description": "Pace for lose/gain (ignored for maintain)."
},
"activity_level": {
"type": "string",
"enum": [
"sedentary",
"light",
"moderate",
"very",
"extra"
]
},
"calorie_target": {
"description": "1000–5000, or \"auto\".",
"anyOf": [
{
"type": "number",
"minimum": 1000,
"maximum": 5000
},
{
"type": "string",
"enum": [
"auto"
]
}
]
},
"protein_target_g": {
"description": "20–400, or \"auto\".",
"anyOf": [
{
"type": "number",
"minimum": 20,
"maximum": 400
},
{
"type": "string",
"enum": [
"auto"
]
}
]
},
"carbs_target_g": {
"description": "20–600, or \"auto\".",
"anyOf": [
{
"type": "number",
"minimum": 20,
"maximum": 600
},
{
"type": "string",
"enum": [
"auto"
]
}
]
},
"fat_target_g": {
"description": "10–250, or \"auto\".",
"anyOf": [
{
"type": "number",
"minimum": 10,
"maximum": 250
},
{
"type": "string",
"enum": [
"auto"
]
}
]
},
"water_goal_glasses": {
"type": "number",
"minimum": 1,
"maximum": 20
},
"fiber_minimum_g": {
"type": "number",
"minimum": 5,
"maximum": 60
},
"sugar_maximum_g": {
"type": "number",
"minimum": 10,
"maximum": 150
},
"sodium_maximum_mg": {
"type": "number",
"minimum": 500,
"maximum": 5000
}
}
}🟡update_reminders(log_reminder, streak_reminder, tz_offset_min)
Turn the user's daily reminders on or off: the evening 'nothing logged today' nudge and the streak reminder. Same as the app's Notifications screen. Needs the user's "Change goals & settings" switch for this connection. The user is notified of the change.
Eingabe-Schema
{
"type": "object",
"properties": {
"log_reminder": {
"type": "boolean"
},
"streak_reminder": {
"type": "boolean"
},
"tz_offset_min": {
"type": "number",
"description": "The user's JS getTimezoneOffset(). Only used if the app has never reported their timezone; then it's required."
}
}
}🔴delete_log(log_id, type)
Delete ONE entry from the user's log: a food, exercise, weigh-in or water entry. Read get_today/get_day first — each item in `recent` carries its `id` and `type`; pass both. Deleting a weigh-in updates their current weight to the newest remaining one. This can't be undone, so confirm with the user first. Needs the user's "Delete entries" switch for this connection (whatevr app → Settings → Connections). The user is notified.
Eingabe-Schema
{
"type": "object",
"properties": {
"log_id": {
"type": "string",
"description": "The item's `id` from get_today/get_day `recent`."
},
"type": {
"type": "string",
"enum": [
"food",
"exercise",
"weight",
"water"
],
"description": "The item's `type` from `recent`."
}
},
"required": [
"log_id",
"type"
]
}🔴update_log(log_id, title, calories, protein_g, carbs_g, ...)
Correct an existing FOOD log: title, nutrition numbers, the ingredient list, meal, or time-of-day. Read get_today/get_day first — each item in `recent` (type "food") carries the `id` to pass here. Send ONLY the fields to change; values are absolute for the item as logged. Two modes, never mixed: send `ingredients` to REPLACE the whole list (include every ingredient you want kept — the log's totals recompute from it, so don't also send calories/macros), OR send top-level numbers to set totals directly — the existing ingredient breakdown then RESCALES proportionally so the parts always sum to the new totals (ingredient names/amounts don't change, so when the COMPOSITION changed — something added or removed — send `ingredients` instead). Note: ANY edit that changes a routine-created log's title or its calories — including an ingredients replacement, since totals recompute — also updates that saved routine. When logged_time changes, the log's meal may re-infer from the new hour unless you set meal explicitly. Requires a full-access token; edit one log at a time.
Eingabe-Schema
{
"type": "object",
"properties": {
"log_id": {
"type": "string",
"description": "The item's `id` from get_today/get_day `recent` (type \"food\" only)."
},
"title": {
"type": "string",
"maxLength": 200
},
"calories": {
"type": "number",
"minimum": 0,
"maximum": 20000
},
"protein_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"carbs_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"fat_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"fiber_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"sugar_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"sodium_mg": {
"type": "number",
"minimum": 0,
"maximum": 100000
},
"meal": {
"type": "string",
"enum": [
"breakfast",
"lunch",
"dinner",
"snack"
]
},
"logged_time": {
"type": "string",
"pattern": "^\\d{2}:\\d{2}$",
"description": "HH:MM 24h — time-of-day only; the log's DATE never changes."
},
"ingredients": {
"type": "array",
"maxItems": 50,
"description": "REPLACES the full ingredient list; totals recompute from it. Cannot be combined with top-level numeric fields.",
"items": {
"type": "object",
"required": [
"name",
"calories"
],
"properties": {
"name": {
"type": "string",
"maxLength": 120
},
"amount": {
"type": "string",
"maxLength": 60
},
"calories": {
"type": "number",
"minimum": 0,
"maximum": 20000
},
"protein_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"carbs_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"fat_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"fiber_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"sugar_g": {
"type": "number",
"minimum": 0,
"maximum": 2000
},
"sodium_mg": {
"type": "number",
"minimum": 0,
"maximum": 100000
}
}
}
}
},
"required": [
"log_id"
]
}Community
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