MCP server

Anatome Official

dev.anatome/anatome

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Log workouts and meals by telling your AI. 873 exercises, muscle diagrams, food lookup.

Website

At a glance

Type

MCP server

Publisher

anatome.dev (verified by the MCP Registry)

Status

Working at last check

Tools

36

Token cost

About 4,806 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http anatome https://anatome.dev/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.anatome]
url = "https://anatome.dev/mcp"

Gemini CLI

{"mcpServers":{"anatome":{"httpUrl":"https://anatome.dev/mcp"}}}

Cursor

{"mcpServers":{"anatome":{"url":"https://anatome.dev/mcp"}}}

VS Code

{"servers":{"anatome":{"type":"http","url":"https://anatome.dev/mcp"}}}

Antigravity

agy mcp add anatome https://anatome.dev/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"anatome","server_url":"https://anatome.dev/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"anatome","server_url":"https://anatome.dev/mcp"}

Tools

• ai_meal_plan ~192 tokens
  Draft a meal plan. Not medical advice. confirm:true required. Pass health constraints so the draft can honour them.
• ai_parse_meal ~77 tokens
  Parse free-text meal description ('2 eggs and toast with butter') into structured items with grams + macros.
• ai_photo_macros ~162 tokens
  Analyze a meal image and estimate foods + macros. Pass exactly one of image_url or image_base64. Vision model, capped separately from the text AI budget (free tier 1/mo).
• ai_workout_plan ~190 tokens
  Draft a workout plan. Not medical advice. confirm:true required. Pass health constraints so the draft can honour them.
• delete_meal Deletes data · ~65 tokens
  Delete a logged meal by id (soft delete, 30-day recovery window). Requires X-App-User-Id.
• delete_workout Deletes data · ~66 tokens
  Delete a logged workout and its sets by id (soft delete, 30-day recovery window).
• export_data ~130 tokens
  Export the signed-in user's data as JSON. Pass `domains` (e.g. meals,workouts) or confirm:true for a full copy. Identity comes from the token, never a caller-supplied user id.
• generate_muscle_image ~101 tokens
  Render an SVG diagram of the human body with muscles highlighted in arbitrary colors. Returns SVG.
• get_body_metrics ~122 tokens
  List the current app user's body metrics / measurements (weight, height, bicep, body fat, etc.), most recent first. Filter by metric_type (e.g. 'weight', 'bicep') and/or a date range. Requires an app user context.
• get_cardio_activities ~75 tokens
  List the current app user's cardio activities (most recent first).
• get_events ~198 tokens
  What changed since a cursor, including DELETES, which a list read cannot show. Returns {seq, entity, id, action}. Pass the last seq back as `since` on the next call to receive only entries after it; omit `since` for a first sync. seq is a global counter: gaps in your feed are other accounts' events,
• get_exercise ~59 tokens
  Fetch one exercise by name, id, or random.
• get_meals ~56 tokens
  List the current app user's meals for a date range.
• get_muscle_recovery ~138 tokens
  Per-muscle 7-day recovery % (Fitbod parity) — the 'what should I train today?' input. Recommends fresh muscles. Pass include_bodyweight=true to add the user's bodyweight to bodyweight exercises so recovery reflects real load.
• get_nutrition_daily ~127 tokens
  Per-day calories, macros, fiber/sugar/sodium/alcohol, water, targets and adherence % over a date range (default: last 7 days). The 'how am I tracking today?' tool. Requires X-App-User-Id.
• get_profile ~63 tokens
  Get the current app user's fitness profile (height, weight, macro + water goals, diet, active sports). Requires an app user context.
• get_progress ~449 tokens
  Any progression metric for the current user — pick one in `metric`. training_load is acute:chronic (>1.5 ramping too fast, <0.8 detraining); muscle_recovery is per-muscle freshness; correlate takes metric_a vs metric_b. Only completed sets count.
• get_routine ~86 tokens
  Fetch a single curated routine by slug with all days + slots fully expanded (exercises, sets, reps ranges, rest, superset groupings). The 'give me a full program' tool.
• get_water ~74 tokens
  List the current app user's water intake entries for a date range. Requires X-App-User-Id.
• get_workouts ~70 tokens
  List the current app user's workouts (most recent first).
• instantiate_routine ~122 tokens
  Schedule the signed-in user's routine into planned workouts from start_date. Coach assignment of another user is REST-only.
• list_muscles ~42 tokens
  List all 23 supported muscle slugs with anatomical names.
• list_routines ~173 tokens
  Browse the curated workout routine library (PPL, upper/lower, full-body, 5x5, etc.). Filter by difficulty, goal, split_type, days_per_week. Returns minimal list.
• log_body_metric ~158 tokens
  Log a body measurement. metric_type: weight (kg), height (cm), body_fat (%), muscle_mass (kg), waist/chest/bicep/forearm/hip/thigh/calf/neck/shoulder (cm), bmi, resting_hr (bpm). Logging weight enables bodyweight-inclusive volume analytics.
• log_cardio ~142 tokens
  Log a cardio activity (run/bike/swim/etc.) for the current app user. Supports duration, distance, HR, power, calories.
• log_meal ~122 tokens
  Log a meal (calories + macros + optional ingredients) for the current app user.
• log_water ~73 tokens
  Log water intake (ml) for the current app user.
• log_workout ~196 tokens
  Log a workout with exercise sets (strength or cardio unified). Requires an app user context.
• lookup_barcode ~52 tokens
  Lookup a food product by EAN/UPC barcode.
• my_training_context ~156 tokens
  Returns a slice of the signed-in user's training picture. Default domains are workouts and today's nutrition. Pass `domains` to add checkin, notes or profile, or `domains=all`. Identity comes from the token, not a caller-supplied user id.
• resolve_exercise ~64 tokens
  Resolve an exercise name into primary/secondary muscle layers for rendering.
• search_exercises ~194 tokens
  Search 873 exercises by name with muscle/equipment/level filters. Rows are lean (name, ext_id, equipment, level, muscles) — enough to pick one; get_exercise returns instructions and media. Paginates: pass next_cursor back as `cursor`.
• search_foods ~76 tokens
  Search food products by name (Open Food Facts + USDA). Returns macros per 100g.
• update_meal ~201 tokens
  Correct a previously logged meal — name, macros, nutrients, portion, date or meal_type. Pass only the fields you want to change. Requires X-App-User-Id.
• update_sets ~198 tokens
  Correct sets already logged, by set id (log_workout returns them). Relabel the exercise, fix weight/reps/rpe, mark completed, add notes, or delete. Selection and change are separate, so a subset can be relabelled. Adding a set is log_workout, not this.
• upsert_profile ~337 tokens
  Upsert the current app user's fitness profile. Set `timezone` first: every daily total, streak and adherence window is bucketed by it and defaults to UTC. `weight` doubles as the fallback bodyweight for bodyweight-inclusive volume when no dated weight metric exists.

Browse

• Website monitoring
• Sports
• Health and medicine
• Food and recipes

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