Deployment

Deployment Options

jellyfin-mcp exposes its MCP server (console script jellyfin-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": {
    "jellyfin-mcp": {
      "command": "uvx",
      "args": ["--from", "jellyfin-mcp", "jellyfin-mcp"],
      "env": {
        "JELLYFIN_URL": "<your-jellyfin_url>",
        "JELLYFIN_BASE_URL": "<your-jellyfin_base_url>",
        "JELLYFIN_ACCESS_TOKEN": "<your-jellyfin_access_token>"
      }
    }
  }
}

2. Streamable-HTTP (local process)

Run the server as a long-lived HTTP process:

uvx --from jellyfin-mcp jellyfin-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": {
    "jellyfin-mcp": {
      "command": "uvx",
      "args": ["--from", "jellyfin-mcp", "jellyfin-mcp", "--transport", "streamable-http", "--port", "8000"],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "JELLYFIN_URL": "<your-jellyfin_url>",
        "JELLYFIN_BASE_URL": "<your-jellyfin_base_url>",
        "JELLYFIN_ACCESS_TOKEN": "<your-jellyfin_access_token>"
      }
    }
  }
}

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

{
  "mcpServers": {
    "jellyfin-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": {
    "jellyfin-mcp": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "TRANSPORT=stdio",
        "-e", "JELLYFIN_URL=<your-jellyfin_url>",
        "-e", "JELLYFIN_BASE_URL=<your-jellyfin_base_url>",
        "-e", "JELLYFIN_ACCESS_TOKEN=<your-jellyfin_access_token>",
        "knucklessg1/jellyfin-mcp:latest"
      ]
    }
  }
}

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

docker run -d --name jellyfin-mcp -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e PORT=8000 \
  -e JELLYFIN_URL="<your-jellyfin_url>" \
  -e JELLYFIN_BASE_URL="<your-jellyfin_base_url>" \
  -e JELLYFIN_ACCESS_TOKEN="<your-jellyfin_access_token>" \
  knucklessg1/jellyfin-mcp:latest
# or, from a clone of this repo:
docker compose -f docker/mcp.compose.yml up -d

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

(c) From a local checkout with uv:

uv run jellyfin-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": {
    "jellyfin-mcp": { "url": "http://jellyfin-mcp.arpa/mcp" }
  }
}

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

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

jellyfin-mcp ships two servers: an MCP server (console script jellyfin-mcp) and an A2A graph agent (console script jellyfin-agent). The MCP server is a typed, deterministic tool surface; the agent wraps it with a Pydantic-AI graph, an optional Web UI, and OpenTelemetry tracing.

Run the MCP server

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

stdio (default)streamable-httpsse

jellyfin-mcp

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

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

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

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

Health check (HTTP transports):

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

Configuration (environment)

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

Var	Default	Meaning
JELLYFIN_URL	http://localhost:8096	Jellyfin server base URL
JELLYFIN_API_KEY	(unset)	API key / access token (preferred auth)
JELLYFIN_USERNAME	admin	Username (credential login, if no key)
JELLYFIN_PASSWORD	(unset)	Password (credential login, if no key)
JELLYFIN_SSL_VERIFY	True	Verify TLS certificates
AUTH_TYPE	apiKey	Authorization flow: apiKey, credentials, or delegated
CONDENSED_JELLYFIN_TOOL	True	Register the condensed Jellyfin tool set

Provide either JELLYFIN_API_KEY or JELLYFIN_USERNAME + JELLYFIN_PASSWORD; when neither is present the server remains inactive rather than failing at import time. Optional OIDC token delegation (ENABLE_DELEGATION, OIDC_TOKEN_ENDPOINT, OIDC_CLIENT_ID, OIDC_CLIENT_SECRET, JELLYFIN_AUDIENCE, DELEGATED_SCOPES) and telemetry (ENABLE_OTEL, OTEL_EXPORTER_OTLP_*) settings are documented in .env.example. Copy it to .env and populate only what you use. Plus HOST / PORT / TRANSPORT for HTTP transports.

Docker Compose

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

services:
  jellyfin-mcp-mcp:
    image: knucklessg1/jellyfin-mcp:latest
    container_name: jellyfin-mcp-mcp
    hostname: jellyfin-mcp-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 JELLYFIN_* values
docker compose -f docker/mcp.compose.yml up -d
docker compose -f docker/mcp.compose.yml logs -f

Agent server

The integrated A2A graph agent is published as the jellyfin-agent console script. It connects to the MCP server over HTTP (MCP_URL), exposes a Web UI / Agent Control Protocol surface, and is driven by a configurable LLM provider.

export JELLYFIN_URL=http://your-jellyfin:8096
export JELLYFIN_API_KEY=your_api_key
jellyfin-agent --provider openai --model-id gpt-4o --host 0.0.0.0 --port 9056

The repo ships docker/agent.compose.yml, which runs the MCP server and the agent together. The agent listens on :9056 and is wired to the MCP server by container name:

services:
  jellyfin-mcp-mcp:
    image: knucklessg1/jellyfin-mcp:latest
    container_name: jellyfin-mcp-mcp
    hostname: jellyfin-mcp-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"

  jellyfin-mcp-agent:
    image: knucklessg1/jellyfin-mcp:latest
    container_name: jellyfin-mcp-agent
    hostname: jellyfin-mcp-agent
    restart: always
    depends_on:
      - jellyfin-mcp-mcp
    env_file:
      - ../.env
    command: [ "jellyfin-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9056
      - MCP_URL=http://jellyfin-mcp-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9056:9056"

docker compose -f docker/agent.compose.yml up -d

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

# Public — automatic Let's Encrypt
jellyfin-mcp.example.com {
    reverse_proxy jellyfin-mcp-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=jellyfin-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 jellyfin-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:

{
  "mcpServers": {
    "jellyfin-mcp": {
      "command": "uv",
      "args": ["run", "jellyfin-mcp"],
      "env": {
        "JELLYFIN_URL": "http://your-jellyfin:8096",
        "JELLYFIN_API_KEY": "your_api_key"
      }
    }
  }
}

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