Deployment

Deployment Options

caddy-mcp exposes its MCP server (console script caddy-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": {
    "caddy-mcp": {
      "command": "uvx",
      "args": ["--from", "caddy-mcp", "caddy-mcp"],
      "env": {
        "CADDY_URL": "<your-caddy_url>",
        "CADDY_TOKEN": "<your-caddy_token>"
      }
    }
  }
}

2. Streamable-HTTP (local process)

Run the server as a long-lived HTTP process:

uvx --from caddy-mcp caddy-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": {
    "caddy-mcp": {
      "command": "uvx",
      "args": ["--from", "caddy-mcp", "caddy-mcp", "--transport", "streamable-http", "--port", "8000"],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "CADDY_URL": "<your-caddy_url>",
        "CADDY_TOKEN": "<your-caddy_token>"
      }
    }
  }
}

…or connect to the already-running process by URL:

{
  "mcpServers": {
    "caddy-mcp": { "url": "http://localhost:8000/mcp" }
  }
}

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": {
    "caddy-mcp": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "TRANSPORT=stdio",
        "-e", "CADDY_URL=<your-caddy_url>",
        "-e", "CADDY_TOKEN=<your-caddy_token>",
        "knucklessg1/caddy-mcp:latest"
      ]
    }
  }
}

(b) Run a local streamable-http container, then connect by URL:

docker run -d --name caddy-mcp -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e PORT=8000 \
  -e CADDY_URL="<your-caddy_url>" \
  -e CADDY_TOKEN="<your-caddy_token>" \
  knucklessg1/caddy-mcp:latest
# or, from a clone of this repo:
docker compose -f docker/mcp.compose.yml up -d

{
  "mcpServers": {
    "caddy-mcp": { "url": "http://localhost:8000/mcp" }
  }
}

(c) From a local checkout with uv:

uv run caddy-mcp --transport streamable-http --port 8000

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:

{
  "mcpServers": {
    "caddy-mcp": { "url": "http://caddy-mcp.arpa/mcp" }
  }
}

Caddy reverse-proxies http://caddy-mcp.arpa to the container's :8000 streamable-http listener; http://caddy-mcp.arpa/health returns {"status":"OK"} when the service is live.

This page covers running caddy-mcp as a long-lived server: the transports, a Docker Compose stack, the optional A2A agent server, putting it behind a Caddy reverse proxy, and giving it a DNS name with Technitium. To provision the Caddy server it connects to, see Backing Platform.

caddy-mcp ships two console scripts: caddy-mcp (the MCP tool surface) and caddy-agent (a Pydantic-AI A2A agent that consumes those tools). Deploy the MCP server on its own for tool access, or pair it with the agent server for a conversational interface.

Run the MCP server

The transport is selected with --transport (or the TRANSPORT env var):

stdio (default)streamable-httpsse

caddy-mcp

For IDE / desktop MCP clients that launch the server as a subprocess.

caddy-mcp --transport streamable-http --host 0.0.0.0 --port 8000

A network server with a /health endpoint and /mcp route.

caddy-mcp --transport sse --host 0.0.0.0 --port 8000

Health check (HTTP transports):

curl -s http://localhost:8000/health        # {"status":"OK"}

Configuration (environment)

caddy-mcp is configured entirely from the environment. The required set:

Var	Default	Meaning
CADDY_URL	http://localhost:2019	Caddy Admin API URL endpoint
CADDY_TOKEN	(unset)	Optional bearer token if the Admin API is secured

Plus HOST / PORT / TRANSPORT for HTTP transports, and CONFIGTOOL (default True) to register the configuration tool set. A template is provided in .env.example — copy it to .env and fill in your values.

Docker Compose

The repo ships docker/mcp.compose.yml. It reads a sibling .env and publishes the HTTP server on :8000:

services:
  caddy-mcp:
    image: knucklessg1/caddy-mcp:latest
    container_name: caddy-mcp
    hostname: caddy-mcp
    restart: always
    env_file:
      - .env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
      - CADDY_URL
      - CADDY_TOKEN
    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 CADDY_URL / CADDY_TOKEN
docker compose -f docker/mcp.compose.yml up -d
docker compose -f docker/mcp.compose.yml logs -f

Agent server

caddy-mcp also ships an A2A agent server via the caddy-agent console script. The agent connects to a running MCP server (its tool source) and exposes a conversational Pydantic-AI interface over HTTP.

# Start the agent; point it at the MCP server's HTTP endpoint
caddy-agent --mcp-url http://caddy-mcp:8000/mcp --host 0.0.0.0 --port 9000

The agent reads its tool registration from mcp_config.json (bundled in the package) or from the --mcp-url of a remote MCP server. Provide a model provider with --provider / --model-id (or the corresponding environment variables). A Compose service for the agent mirrors the MCP service, wiring MCP_URL at its own published port (for example :9000):

# docker/agent.compose.yml
services:
  caddy-agent:
    image: knucklessg1/caddy-mcp:latest
    container_name: caddy-agent
    hostname: caddy-agent
    restart: always
    entrypoint: ["caddy-agent"]
    env_file:
      - .env
    environment:
      - PYTHONUNBUFFERED=1
      - MCP_URL=http://caddy-mcp:8000/mcp
      - HOST=0.0.0.0
      - PORT=9000
    ports:
      - "9000:9000"
    depends_on:
      - caddy-mcp

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
caddy-mcp.arpa {
    tls internal
    reverse_proxy caddy-mcp:8000
}

# Public — automatic Let's Encrypt
caddy-mcp.example.com {
    reverse_proxy caddy-mcp:8000
}

Reload Caddy:

docker compose -f services/caddy/compose.yml exec caddy caddy reload --config /etc/caddy/Caddyfile

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=caddy-mcp.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 caddy-mcp.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 (multiplexer nickname cd):

{
  "mcpServers": {
    "caddy-mcp": {
      "command": "uv",
      "args": ["run", "caddy-mcp"],
      "env": {
        "CADDY_URL": "http://your-caddy:2019",
        "CADDY_TOKEN": ""
      }
    }
  }
}

For a remote HTTP server, point the client at http://caddy-mcp.arpa/mcp instead.