# whatevr Muse connector

From musedirectory.ai, the independent directory of Meta Muse connectors. Not affiliated with Meta.

## whatevr

Log meals, exercise, and weight; track calories and macros from any AI assistant.

- Record: https://musedirectory.ai/connector/whatevr
- Category: Health
- Developer: trywhatevr.app (https://trywhatevr.app/connect)
- Muse status: Extra setup. Not in Muse's Connectors list yet. Muse can still use it: its page gives you a request to paste into Muse.
- Health: Working, 1242ms, checked 2026-10-30T00:21:10Z
- Endpoint: https://api.trywhatevr.app/mcp
- Auth: No account needed; Pricing: unknown
- Screening: Screened, no issues found (2026-10-30T00:21:10Z)
- Source: Found in the official MCP Registry (app.trywhatevr/whatevr) https://registry.modelcontextprotocol.io/v0/servers?search=app.trywhatevr%2Fwhatevr

Connects to the whatevr iPhone app to log food, drinks, water, exercise, and weigh-ins using natural language. Muse can read your daily nutrition totals, macros, weight trends, and food history, and correct logged items.

Example request: "Log that I had two eggs and toast for breakfast this morning and show me my calorie total for today."

How to connect: Not in Muse's Connectors list yet, but Muse can still use it. Paste this into Muse: "Use whatevr to help me. It is a free service with an MCP server at https://api.trywhatevr.app/mcp. It does not need an API key. Ask me before you share anything with it." Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.

Tools:
- log_anything: 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. Des
- log_exercise: Log structured exercise with a known calorie burn. For an ESTIMATED burn from plain text, use log_anything ('30 minute run').
- log_weight: Log a weigh-in in pounds. The newest reading also becomes the user's current weight (drives their calorie targets).
- log_water: Log water — glasses (8 fl oz each) or an exact amount_fl_oz. Defaults to 1 glass.
- get_today: 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.
- get_day: 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.
- get_progress: Daily calorie/macro/exercise TOTALS over time + the weight trend (no food item names — use get_day for what was actually eaten).
- get_me: Who the user is: name/handle/email, health profile, calorie/macro targets, and derived metrics.
- update_log: 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 mod

Screening checks:
- MCP handshake: pass (Answered in 1242ms)
- Domain against threat feeds (Cloudflare security DNS): pass (api.trywhatevr.app, trywhatevr.app not flagged)
- Published packages against the OSV malicious-package database: n/a (No npm or PyPI package published)
- Hidden instructions or invisible characters in tool text: pass (9 tools read, nothing found)
- Inputs asking for passwords, card numbers or seed phrases: pass (None found)
- Domain and redirects: pass (Domain registered 115 days ago)
- AI review of purpose and tool behavior: pass (No concerns)