dev.anatome/anatome · v2.6.0 · cloud-and-infra
dev.anatome/anatome is healthy: it completed an MCP handshake in 4339 ms and exposes 36 tools.
Log workouts and meals by telling your AI. 873 exercises, muscle diagrams, food lookup.
Endpoint: https://anatome.dev/mcp (streamable-http)
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| Users (last 30 days) | 0 |
| Handshake | Healthy |
| Auth needed | None |
| Tool count | 36 |
| Latency | 4339 ms |
| Last checked (UTC) | 2026-10-14T23:10:46.791Z |
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-10-14T23:10:46.791Z | Healthy | 4339 |
search_exercises | 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`. |
get_exercise | Fetch one exercise by name, id, or random. |
resolve_exercise | Resolve an exercise name into primary/secondary muscle layers for rendering. |
list_muscles | List all 23 supported muscle slugs with anatomical names. |
generate_muscle_image | Render an SVG diagram of the human body with muscles highlighted in arbitrary colors. Returns SVG. |
list_routines | 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. |
get_routine | 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. |
instantiate_routine | Schedule the signed-in user's routine into planned workouts from start_date. Coach assignment of another user is REST-only. |
search_foods | Search food products by name (Open Food Facts + USDA). Returns macros per 100g. |
lookup_barcode | Lookup a food product by EAN/UPC barcode. |
log_workout | Log a workout with exercise sets (strength or cardio unified). Requires an app user context. |
get_workouts | List the current app user's workouts (most recent first). |
log_meal | Log a meal (calories + macros + optional ingredients) for the current app user. |
get_meals | List the current app user's meals for a date range. |
log_water | Log water intake (ml) for the current app user. |
log_body_metric | 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. |
get_body_metrics | 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. |
upsert_profile | 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. |
get_profile | Get the current app user's fitness profile (height, weight, macro + water goals, diet, active sports). Requires an app user context. |
log_cardio | Log a cardio activity (run/bike/swim/etc.) for the current app user. Supports duration, distance, HR, power, calories. |
get_cardio_activities | List the current app user's cardio activities (most recent first). |
ai_parse_meal | Parse free-text meal description ('2 eggs and toast with butter') into structured items with grams + macros. |
ai_photo_macros | 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_meal_plan | Draft a meal plan. Not medical advice. confirm:true required. Pass health constraints so the draft can honour them. |
ai_workout_plan | Draft a workout plan. Not medical advice. confirm:true required. Pass health constraints so the draft can honour them. |
get_muscle_recovery | 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 | 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_water | List the current app user's water intake entries for a date range. Requires X-App-User-Id. |
update_meal | 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. |
delete_meal | Delete a logged meal by id (soft delete, 30-day recovery window). Requires X-App-User-Id. |
update_sets | 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. |
delete_workout | Delete a logged workout and its sets by id (soft delete, 30-day recovery window). |
export_data | 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. |
get_events | 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, |
my_training_context | 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. |
get_progress | 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. |