# MCP Logger
A Python/`uv` + FastMCP server for logging workouts, nutrition, and body metrics. Single-user local SQLite database with stdio MCP interface.
- this was entirely vibe coded
## Features
- **Workouts**: Flexible `workout -> exercises[] -> sets[]` structure with tags, notes, RPE/RIR, distances, unilateral sides, etc.
- **Nutrition**: Cronometer/MyFitnessPal-style logging with meals and OpenNutrition-backed food snapshots.
- **Body Metrics**: Weight and customizable skinfold tracking.
- **Search**: Cross-domain search across all data.
## Tools
### Workout Tools
- `log_workout` - Log a complete workout with exercises and sets
- `get_workouts` - Query workouts with filters (date range, type, tag)
- `get_last_workout` - Get most recent workout by type or tag
- `get_exercise_history` - Get history for a specific exercise
### Nutrition Tools
- `upsert_nutrition_day` - Create/update a nutrition day
- `upsert_meal` - Create/update a meal within a day
- `add_or_update_meal_item` - Add/update food item (use with OpenNutrition MCP)
- `get_nutrition_day` - Get complete day with meals, items, and totals
- `get_nutrition_days_summary` - Get summaries for a date range
- `delete_meal_item`, `delete_meal`, `delete_nutrition_day` - Delete operations
### Body Metrics Tools
- `log_body_metrics` - Log weight and skinfolds
- `get_body_metrics` - Get body metrics with skinfolds
### Search
- `search_logs` - Search across workouts, nutrition, and body data
## Installation & Running
```bash
# Install dependencies
uv pip install -e .
# Run the MCP server (stdio interface)
uv run python -m src.main
```
## MCP Config Example
Add to your MCP configuration:
```json
{
"mcpServers": {
"logger": {
"command": "uv",
"args": ["run", "python", "-m", "src.main"],
"cwd": "/path/to/mcp-logger"
}
}
}
```
## Nutrition Workflow with OpenNutrition MCP
1. AI uses OpenNutrition MCP to search for foods (`search-food-by-name`, `get-food-by-id`)
2. AI computes macros for the desired serving size
3. AI calls `add_or_update_meal_item` with food_id and calculated macros
## Workout Planning
The AI can call `get_last_workout` or `get_exercise_history` to retrieve past sessions, then generate suggested workouts. Progression logic lives in the client AI, not this server.
## Database
Data is stored in `mcp_logger.db` (SQLite) in the project root.
## Example Usage
### Log a Workout with Exercises
```json
{
"date_time": "2026-01-06T18:30:00",
"workout_type": "Strength",
"tags": ["olympic", "speed"],
"notes": "Great session",
"exercises": [
{
"name": "Power Clean",
"category": "Olympic Lift",
"notes": "From blocks",
"sets": [
{ "reps": 3, "weight_lbs": 185 },
{ "reps": 2, "weight_lbs": 195 },
{ "reps": 1, "weight_lbs": 205 }
]
},
{
"name": "Sprint Starts",
"category": "Sprint",
"notes": "3 point stance",
"sets": [{ "reps": 6, "distance_yards": 20 }]
},
{
"name": "Single Leg Box Jumps",
"category": "Plyometric",
"notes": "5 sets of 2 each leg",
"sets": [{ "reps": 10, "side": "both" }]
}
]
}
```
### Set Fields
Each set can include:
- `reps`: Number of repetitions (int or float)
- `weight_kg` / `weight_lbs`: Weight in kg or lbs
- `distance_m` / `distance_yards`: Distance for running/rowing
- `duration_s`: Duration in seconds
- `side`: "left", "right", or "both" (for unilateral exercises)
- `rpe`: Rate of Perceived Exertion (1-10)
- `rir`: Reps In Reserve (0-5)
- `is_warmup`: Boolean for warmup sets
- `set_index`: Manual set ordering (defaults to order inserted)
### Log Body Metrics
```json
{
"date": "2026-01-06",
"body_weight_kg": 85.5,
"skinfolds": {
"chest": 12,
"abdomen": 18,
"thigh": 15,
"tricep": 10,
"subscapular": 14,
"suprailiac": 16,
"midaxillary": 11
},
"notes": "Morning measurement"
}
```
# MCP-logger
