Skip to content

Deployment

Deployment Options

atlassian-agent exposes its MCP server (console script atlassian-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": {
    "atlassian-mcp": {
      "command": "uvx",
      "args": ["--from", "atlassian-agent", "atlassian-mcp"],
      "env": {
        "ATLASSIAN_AGENT_URL": "<your-atlassian_agent_url>",
        "ATLASSIAN_AGENT_TOKEN": "<your-atlassian_agent_token>",
        "ATLASSIAN_AGENT_USER": "<your-atlassian_agent_user>"
      }
    }
  }
}

2. Streamable-HTTP (local process)

Run the server as a long-lived HTTP process:

uvx --from atlassian-agent atlassian-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": {
    "atlassian-mcp": {
      "command": "uvx",
      "args": ["--from", "atlassian-agent", "atlassian-mcp", "--transport", "streamable-http", "--port", "8000"],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "ATLASSIAN_AGENT_URL": "<your-atlassian_agent_url>",
        "ATLASSIAN_AGENT_TOKEN": "<your-atlassian_agent_token>",
        "ATLASSIAN_AGENT_USER": "<your-atlassian_agent_user>"
      }
    }
  }
}

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

{
  "mcpServers": {
    "atlassian-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": {
    "atlassian-mcp": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "TRANSPORT=stdio",
        "-e", "ATLASSIAN_AGENT_URL=<your-atlassian_agent_url>",
        "-e", "ATLASSIAN_AGENT_TOKEN=<your-atlassian_agent_token>",
        "-e", "ATLASSIAN_AGENT_USER=<your-atlassian_agent_user>",
        "knucklessg1/atlassian-agent:latest"
      ]
    }
  }
}

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

docker run -d --name atlassian-mcp -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e PORT=8000 \
  -e ATLASSIAN_AGENT_URL="<your-atlassian_agent_url>" \
  -e ATLASSIAN_AGENT_TOKEN="<your-atlassian_agent_token>" \
  -e ATLASSIAN_AGENT_USER="<your-atlassian_agent_user>" \
  knucklessg1/atlassian-agent:latest
# or, from a clone of this repo:
docker compose -f docker/mcp.compose.yml up -d

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

(c) From a local checkout with uv:

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

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

This page covers running atlassian-agent as a long-lived service: the transports, a Docker Compose stack, the A2A agent server, putting it behind a Caddy reverse proxy, and giving it a DNS name with Technitium.

atlassian-agent ships two console scripts: an MCP server (atlassian-mcp) exposing a typed, deterministic Atlassian tool surface, and an A2A agent server (atlassian-agent) that drives those tools conversationally. Deploy the MCP server on its own, or deploy both together as a combined stack.

Run the MCP server

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

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

atlassian-mcp --transport streamable-http --host 0.0.0.0 --port 8000
A network server with a /health endpoint and /mcp route.

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

Health check (HTTP transports):

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

Configuration (environment)

atlassian-agent is configured entirely from the environment. The required set for the shared (Cloud) connection:

Var Default Meaning
ATLASSIAN_AGENT_URL http://localhost:8080 Atlassian base URL (e.g. https://your-company.atlassian.net)
ATLASSIAN_AGENT_USER — Account email / username
ATLASSIAN_AGENT_TOKEN — API token (Cloud) or password / token (Server)
ATLASSIAN_AGENT_VERIFY True Verify TLS
HOST 0.0.0.0 Bind address (HTTP transports)
PORT 8000 Listen port (HTTP transports)
TRANSPORT stdio stdio, streamable-http, or sse

Jira and Confluence Server / Data Center instances may be configured separately with their own credentials (ATLASSIAN_JIRA_SERVER_URL / _USER / _TOKEN / _VERIFY, and ATLASSIAN_CONFLUENCE_SERVER_URL / _USER / _TOKEN / _VERIFY). Each tool group additionally has a *_TOOL toggle (for example JIRA_ISSUE_TOOL, CONFLUENCE_PAGE_TOOL, ATLASSIAN_ADMIN_TOOL) to register only the surface you need. The full set, grouped by product, is documented in .env.example. Copy it to .env and populate only what you use; tools whose credentials are absent remain inactive.

Backing Service

Atlassian Jira and Confluence are managed as Atlassian Cloud (a SaaS platform) or as self-operated Server / Data Center products. atlassian-agent is a connector, not a host for those systems, so there is no local backing-platform recipe — only connection configuration is required. Provision an API token from your Atlassian account and point ATLASSIAN_AGENT_URL, ATLASSIAN_AGENT_USER, and ATLASSIAN_AGENT_TOKEN at the instance you intend to manage.

Docker Compose

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

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

Run the A2A agent server

The atlassian-agent console script starts a Pydantic-AI agent that consumes the MCP tool surface and exposes an A2A endpoint (and an optional web UI). Point it at a running MCP server with MCP_URL and select a model provider:

export MCP_URL=http://localhost:8000/mcp
atlassian-agent --provider openai --model-id gpt-4o --api-key sk-...

The repository ships docker/agent.compose.yml, which deploys the MCP server and the agent together. The agent publishes on :9004 and reaches the MCP server by container name:

services:
  atlassian-agent-mcp:
    image: knucklessg1/atlassian-agent:latest
    hostname: atlassian-agent-mcp
    env_file: [../.env]
    environment:
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports: ["8000:8000"]

  atlassian-agent-agent:
    image: knucklessg1/atlassian-agent:latest
    command: ["atlassian-agent"]
    depends_on: [atlassian-agent-mcp]
    env_file: [../.env]
    environment:
      - HOST=0.0.0.0
      - PORT=9004
      - MCP_URL=http://atlassian-agent-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
    ports: ["9004:9004"]

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
atlassian-agent.arpa {
    tls internal
    reverse_proxy atlassian-agent-mcp:8000
}

# Public — automatic Let's Encrypt
atlassian-agent.example.com {
    reverse_proxy atlassian-agent-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=atlassian-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 atlassian-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": {
    "atlassian-agent": {
      "command": "uv",
      "args": ["run", "atlassian-mcp"],
      "env": {
        "ATLASSIAN_AGENT_URL": "https://your-company.atlassian.net",
        "ATLASSIAN_AGENT_USER": "your-email@example.com",
        "ATLASSIAN_AGENT_TOKEN": "your_api_token"
      }
    }
  }
}

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