Wger Workout Manager — exercise database, workout routines, nutrition plans, body measurements, and progress tracking.
Project description
Wger - A2A | AG-UI | MCP
Version: 0.1.6
Overview
Wger MCP Server + A2A Agent
Wger Workout Manager — exercise database, workout routines, nutrition plans, body measurements, and progress tracking.
This repository is actively maintained - Contributions are welcome!
MCP
Using as an MCP Server
The MCP Server can be run in two modes: stdio (for local testing) or http (for networked access).
Environment Variables
WGER_INSTANCE: The URL of the target service.WGER_ACCESS_TOKEN: The API token or access token.
Run in stdio mode (default):
export WGER_INSTANCE="http://localhost:8080"
export WGER_ACCESS_TOKEN="your_token"
wger-mcp --transport "stdio"
Run in HTTP mode:
export WGER_INSTANCE="http://localhost:8080"
export WGER_ACCESS_TOKEN="your_token"
wger-mcp --transport "http" --host "0.0.0.0" --port "8000"
A2A Agent
Run A2A Server
export WGER_INSTANCE="http://localhost:8080"
export WGER_ACCESS_TOKEN="your_token"
wger-agent --provider openai --model-id gpt-4o --api-key sk-...
Docker
Build
docker build -t wger-agent .
Run MCP Server
docker run -d \
--name wger-agent \
-p 8000:8000 \
-e TRANSPORT=http \
-e WGER_INSTANCE="http://your-service:8080" \
-e WGER_ACCESS_TOKEN="your_token" \
knucklessg1/wger-agent:latest
Deploy with Docker Compose
services:
wger-agent:
image: knucklessg1/wger-agent:latest
environment:
- HOST=0.0.0.0
- PORT=8000
- TRANSPORT=http
- WGER_INSTANCE=http://your-service:8080
- WGER_ACCESS_TOKEN=your_token
ports:
- 8000:8000
Configure mcp.json for AI Integration (e.g. Claude Desktop)
{
"mcpServers": {
"wger": {
"command": "uv",
"args": [
"run",
"--with",
"wger-agent",
"wger-mcp"
],
"env": {
"WGER_INSTANCE": "http://your-service:8080",
"WGER_ACCESS_TOKEN": "your_token"
}
}
}
}
Install Python Package
python -m pip install wger-agent
uv pip install wger-agent
Repository Owners
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