DataBody CLI

A command-line interface for DataBody health and fitness tracking, designed for local LLM tool use. All commands output JSON for easy parsing.

Installation

npm install -g databody-cli

Or run directly:

npx databody-cli <command>

Authentication

Browser OAuth (recommended)

databody auth login

Opens your browser for secure OAuth login. Tokens are saved to ~/.databody_token.json.

Cross-machine auth

Authenticate on one machine and transfer the token to another:

# Machine A (has your account):
databody auth login
databody auth export-token --compact
# → prints a base64 string

# Machine B:
databody auth import-token <base64_string>

Or set a token directly:

databody auth login --token <access_token> --refresh-token <refresh_token>

Other auth methods

databody auth login --password -e [email protected] -p secret  # Password grant
databody auth status                                           # Check auth status
databody auth logout                                           # Clear tokens

Quick Start

databody summary                    # Today's health summary
databody today                      # Today's nutrition logs
databody chat "What should I eat?"  # Chat with AI coach
databody log breakfast '[{"name":"eggs","calories":140,"protein_grams":12,"carbs_grams":1,"fat_grams":10}]'

Commands

Health

databody health summary              # Dashboard: macros, goals, workouts, trends
databody health history --days 90    # Historical health stats

Nutrition

databody nutrition today
databody nutrition history --start 2026-01-01 --end 2026-01-31
databody nutrition get <log_id>
databody nutrition log --meal breakfast --items '[{...}]'
databody nutrition log --meal lunch --stdin           # Read items from stdin
databody nutrition update <log_id> --meal lunch
databody nutrition delete <log_id>
databody nutrition add-item <log_id> --name "toast" --calories 80 --protein 3 --carbs 15 --fat 1
databody nutrition delete-item <log_id> <item_id>

Food Search

databody food search "chicken breast"
databody food search "yogurt" --source usda --page 2
databody food details <food_id>
databody food barcode <barcode>
databody food favorites
databody food add-favorite --name "Greek Yogurt" --calories 100 --protein 17 --carbs 6 --fat 1
databody food remove-favorite <id>
databody food recents

Goals

databody goals current
databody goals list
databody goals create --calories 2000 --protein 180 --carbs 200 --fat 67 --strategy cut
databody goals update <id> --calories 2200
databody goals delete <id>
databody goals calculate <id>        # Recalculate from current health data

Workouts

databody workouts recent --limit 20
databody workouts list --start 2026-01-01 --type running
databody workouts create --type running --started-at 2026-03-14T08:00:00Z --duration 45 --calories 400
databody workouts update <id> --calories 450
databody workouts delete <id>

AI

databody ai chat "What should I eat for dinner?"
databody ai chat "Plan meals" --thread 5 --household 1
databody ai suggestions --preferences "low carb"
databody ai analyze-photo --file /path/to/meal.jpg
databody ai parse "2 eggs with toast and butter"
databody ai expand --meal "Stir Fry" --ingredients "chicken,broccoli,rice"
databody ai greeting
databody ai token-usage
databody ai chat-history --thread 5

Chat Threads

databody threads list
databody threads get <id>
databody threads create --title "Meal Planning"
databody threads delete <id>
databody threads generate-title <id>

Notes

databody notes list
databody notes create "I have a home gym with dumbbells"
databody notes update <id> "Updated note"
databody notes delete <id>

Households

databody households list
databody households summary <id> --days 7
databody households create "Family Name"
databody households update <id> --name "New Name"
databody households delete <id>
databody households members <id>
databody households remove-member <id> <member_id>

Invites

databody invites list <household_id>
databody invites pending
databody invites create <household_id> --email "[email protected]"
databody invites accept <id>
databody invites decline <id>

User Profile

databody user profile
databody user update --name "Nick" --timezone "America/New_York"
databody user change-password --current "old" --new "new"
databody user change-email --password "pass" --email "[email protected]"

Output

All commands output JSON to stdout. Use --pretty for formatted output:

databody health summary --pretty

Exit codes:

• 0 — Success
• 1 — API or application error
• 2 — Authentication required

Environment Variables

| Variable | Description | Default | |----------|-------------|---------| | DATABODY_API_URL | DataBody API URL | https://databody.ai | | DATABODY_CALLBACK_PORT | OAuth callback port | 8787 |

For local development:

export DATABODY_API_URL=http://localhost:3000

Development

git clone https://github.com/databody-ai/databody-cli.git
cd databody-cli
npm install
npm run build
npm link              # Makes 'databody' available globally

npm run dev           # Watch mode
npm run typecheck     # Type checking
npm test              # Run tests

Links

• DataBody App
• DataBody MCP Server
• GitHub Issues