Deployment¶
Deployment Options¶
wger-agent exposes its MCP server (console script wger-mcp) four ways. Pick the row that
matches where the server runs relative to your MCP client, then copy the matching
mcp_config.json below. Replace the <your-…> placeholders with the values from the Configuration / Environment Variables section.
| # | Option | Transport | Where it runs | mcp_config.json key |
|---|---|---|---|---|
| 1 | stdio | stdio |
client launches a subprocess | command |
| 2 | Streamable-HTTP (local) | streamable-http |
a local network port | command or url |
| 3 | Local container / uv | stdio or streamable-http |
Docker / Podman / uv on this host | command or url |
| 4 | Remote URL | streamable-http |
a remote host behind Caddy | url |
1. stdio (local subprocess)¶
The client launches the server over stdio via uvx — best for local IDEs
(Cursor, Claude Desktop, VS Code):
{
"mcpServers": {
"wger-mcp": {
"command": "uvx",
"args": ["--from", "wger-agent", "wger-mcp"],
"env": {
"WGER_URL": "<your-wger_url>",
"WGER_API_KEY": "<your-wger_api_key>"
}
}
}
}
2. Streamable-HTTP (local process)¶
Run the server as a long-lived HTTP process:
uvx --from wger-agent wger-mcp --transport streamable-http --host 0.0.0.0 --port 8000
curl -s http://localhost:8000/health # {"status":"OK"}
Then either let the client launch it:
{
"mcpServers": {
"wger-mcp": {
"command": "uvx",
"args": ["--from", "wger-agent", "wger-mcp", "--transport", "streamable-http", "--port", "8000"],
"env": {
"TRANSPORT": "streamable-http",
"HOST": "0.0.0.0",
"PORT": "8000",
"WGER_URL": "<your-wger_url>",
"WGER_API_KEY": "<your-wger_api_key>"
}
}
}
}
…or connect to the already-running process by URL:
3. Local container / uv¶
(a) Launch a container directly from mcp_config.json (stdio over the container —
no ports to manage). Swap docker for podman for a daemonless runtime:
{
"mcpServers": {
"wger-mcp": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"-e", "TRANSPORT=stdio",
"-e", "WGER_URL=<your-wger_url>",
"-e", "WGER_API_KEY=<your-wger_api_key>",
"knucklessg1/wger-agent:latest"
]
}
}
}
(b) Run a local streamable-http container, then connect by URL:
docker run -d --name wger-mcp -p 8000:8000 \
-e TRANSPORT=streamable-http \
-e PORT=8000 \
-e WGER_URL="<your-wger_url>" \
-e WGER_API_KEY="<your-wger_api_key>" \
knucklessg1/wger-agent:latest
# or, from a clone of this repo:
docker compose -f docker/mcp.compose.yml up -d
(c) From a local checkout with uv:
4. Remote URL (deployed behind Caddy)¶
When the server is deployed remotely (e.g. as a Docker service) and published through
Caddy on the internal *.arpa zone, connect with the "url" key — no local process or
image required:
Caddy reverse-proxies http://wger-mcp.arpa to the container's :8000
streamable-http listener; http://wger-mcp.arpa/health returns
{"status":"OK"} when the service is live.
This page covers running wger-agent as long-lived servers: the transports, a Docker
Compose stack, the optional graph agent, putting it behind a Caddy reverse proxy, and
giving it a DNS name with Technitium. To provision the Wger platform it connects
to, see Backing Platform.
wger-agentships two console scripts: an MCP server (wger-mcp) and a Pydantic-AI graph agent (wger-agent). The MCP server is a typed, deterministic tool surface; the agent server orchestrates those tools behind the Agent Control Protocol and the Agent Web UI.
Run the MCP server¶
The transport is selected with --transport (or the TRANSPORT env var):
Health check (HTTP transports):
Configuration (environment)¶
wger-agent is configured entirely from the environment. The required set:
| Var | Default | Meaning |
|---|---|---|
WGER_URL |
https://wger.de |
Wger instance base URL |
WGER_API_KEY |
(none) | Wger API token |
WGER_DEFAULT_EMAIL |
(none) | Default account email |
WGER_DEFAULT_PASSWORD |
(none) | Default account password |
TRANSPORT |
stdio |
stdio, streamable-http, or sse |
HOST |
0.0.0.0 |
Bind address (HTTP transports) |
PORT |
8000 |
Bind port (HTTP transports) |
Each tool domain is registered through a toggle variable — ROUTINETOOL,
ROUTINECONFIGTOOL, EXERCISETOOL, WORKOUTTOOL, NUTRITIONTOOL, BODYTOOL,
USERTOOL (all default True). Telemetry (ENABLE_OTEL, OTLP exporter settings)
and access governance (EUNOMIA_TYPE, EUNOMIA_POLICY_FILE) are configured the same
way. The full set is documented in
.env.example.
Copy it to .env and populate only what you use; the connector remains inactive when
credentials are absent.
Docker Compose¶
The repo ships docker/mcp.compose.yml.
It reads a sibling .env and publishes the HTTP server on :8000:
services:
wger-agent-mcp:
image: knucklessg1/wger-agent:latest
container_name: wger-agent-mcp
hostname: wger-agent-mcp
restart: always
env_file:
- ../.env
environment:
- PYTHONUNBUFFERED=1
- HOST=0.0.0.0
- PORT=8000
- TRANSPORT=streamable-http
ports:
- "8000:8000"
healthcheck:
test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
interval: 30s
timeout: 10s
retries: 3
cp .env.example .env # then edit WGER_* values
docker compose -f docker/mcp.compose.yml up -d
docker compose -f docker/mcp.compose.yml logs -f
Agent server¶
To run the integrated Pydantic-AI graph agent, use the wger-agent console script.
It connects to the MCP server over MCP_URL, exposes the Agent Control Protocol and
the Agent Web UI on its own port (9004 by convention), and routes each request to
the relevant tool domain.
export WGER_URL=https://your-wger:8000
export WGER_API_KEY=your_api_key
export MCP_URL=http://wger-agent-mcp:8000/mcp
wger-agent --provider openai --model-id gpt-4o
The repo ships
docker/agent.compose.yml,
which deploys the MCP server and the agent together on one network so the agent
reaches the MCP server by container name:
services:
wger-agent-mcp:
image: knucklessg1/wger-agent:latest
container_name: wger-agent-mcp
hostname: wger-agent-mcp
restart: always
env_file:
- ../.env
environment:
- PYTHONUNBUFFERED=1
- HOST=0.0.0.0
- PORT=8000
- TRANSPORT=streamable-http
ports:
- "8000:8000"
wger-agent-agent:
image: knucklessg1/wger-agent:latest
container_name: wger-agent-agent
hostname: wger-agent-agent
restart: always
depends_on:
- wger-agent-mcp
env_file:
- ../.env
command: ["wger-agent"]
environment:
- PYTHONUNBUFFERED=1
- HOST=0.0.0.0
- PORT=9004
- MCP_URL=http://wger-agent-mcp:8000/mcp
- PROVIDER=${PROVIDER:-openai}
- MODEL_ID=${MODEL_ID:-gpt-4o}
- ENABLE_WEB_UI=True
- ENABLE_OTEL=True
ports:
- "9004:9004"
Behind a Caddy reverse proxy¶
Expose the HTTP server on a hostname with automatic TLS. Add to your Caddyfile:
# Internal (self-signed) — homelab .arpa zone
wger-agent.arpa {
tls internal
reverse_proxy wger-agent-mcp:8000
}
Reload Caddy:
DNS with Technitium¶
Point the hostname at the host running Caddy. Via the Technitium API:
curl -s "http://technitium.arpa:5380/api/zones/records/add" \
--data-urlencode "token=$TECHNITIUM_DNS_TOKEN" \
--data-urlencode "domain=wger-agent.arpa" \
--data-urlencode "zone=arpa" \
--data-urlencode "type=A" \
--data-urlencode "ipAddress=10.0.0.10" \
--data-urlencode "ttl=3600"
…or add an A record wger-agent.arpa → <caddy-host-ip> in the Technitium web
console (http://technitium.arpa:5380). The ecosystem
technitium-dns-mcp automates
this as a tool.
Register with an MCP client¶
Add to your client's mcp_config.json:
{
"mcpServers": {
"wger-agent": {
"command": "uvx",
"args": ["--from", "wger-agent", "wger-mcp"],
"env": {
"WGER_URL": "https://your-wger:8000",
"WGER_API_KEY": "your_api_key"
}
}
}
}
For a remote HTTP server, point the client at http://wger-agent.arpa/mcp instead.