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MCP server for Docker — containers, health checks, Compose, logs, monitoring for AI agents
MCP server for Docker — containers, health checks, Compose, logs, monitoring for AI agents
This is a well-architected Docker MCP server with strong input validation via Zod schemas and output sanitization. However, there are several moderate concerns: the Cloudflare Worker variant exposes managed Docker infrastructure via HTTP API with per-user isolation that may not be cryptographically sound, the server has inherent Docker socket access with no per-operation fine-grained authorization, and some code quality issues around error handling and secret management in the Worker tier. Permissions align with the stated purpose of Docker management for AI agents, but users should understand the threat model clearly. Supply chain analysis found 4 known vulnerabilities in dependencies (1 critical, 3 high severity). Package verification found 1 issue.
5 files analyzed · 12 issues found
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Set these up before or after installing:
Environment variable: DOCKER_HOST
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-friendlygeorge-docker-mcp-server": {
"env": {
"DOCKER_HOST": "your-docker-host-here"
},
"args": [
"-y",
"@supernova123/docker-mcp-server"
],
"command": "npx"
}
}
}From the project's GitHub README.
The Docker MCP server designed for agents that need their containers to stay running.
Runs locally. No API keys. No paid plan. Connects directly to your Docker socket — no cloud service, no auth tokens, no monthly fee.
Without this: Your agent deploys a container, it crashes at 3am, and nobody notices until the user complains. Compose stacks drift. Health checks are manual. Logs are scattered across terminals.
With this: Your agent checks health, watches for readiness, restarts crashed containers, and follows logs, all through a single MCP interface. Containers stay running because your agent knows how to keep them running.
There are 11+ Docker MCP servers on npm. Most are stale, GPL-licensed, or only cover basic CRUD. This one is different:
| This server | ckreiling/mcp-server-docker | docker/hub-mcp | |
|---|---|---|---|
| License | MIT | GPL-3.0 | Apache-2.0 |
| Runs locally | ✅ No API keys, no paid plan | ✅ | ❌ Requires Docker Hub auth |
| Last updated | Active | Jun 2025 (stale) | Active |
| Health checks | ✅ HTTP/TCP/exec probes | ❌ | ❌ |
| Auto-restart | ✅ set_restart_policy | ❌ | ❌ |
| Compose lifecycle | ✅ up/down/ps/logs/restart | ❌ | ❌ |
| Log streaming | ✅ tail + timestamp filter | Basic | Basic |
| Fleet monitoring | ✅ 6 fleet tools (status, stats, events, logs, thresholds, dashboard) | ❌ | ❌ |
| Agent positioning | ✅ Built for agents | Generic Docker | Registry API |
Agent-managed deployments: Your agent deploys a new version, checks health, waits for readiness, then switches traffic. If the health check fails, it auto-rolls back.
Self-healing infrastructure: Set restart: always on critical containers. Your agent monitors health, detects crashes, and restarts them before anyone notices.
Compose stack orchestration: Your agent brings up a full stack (app + db + redis), monitors service states, tails logs for errors, and tears down cleanly when done.
Debugging sessions: Your agent execs into a container, runs diagnostics, streams logs with timestamp filters, and captures stats — all without SSH.
Here's what an agent actually does with this server during a deployment:
1. Deploy: run_container(image="myapp:v2", ports={8080:80})
2. Health check: check_health(container="myapp", type="http", path="/ready")
3. Wait: watch_health(container="myapp", timeout=30)
4. Monitor: fleet_status() → see all containers, health states, uptime
5. Watch: watch_events(window=60) → detect crashes, restarts, health changes
6. Debug: search_logs(pattern="ERROR", containers=["myapp"])
7. Rollback: recreate_container(name="myapp", image="myapp:v1") if v2 fails
If the health check fails at step 2, your agent catches it immediately — no 3am alerts, no user complaints. If the container crashes at step 5, set_restart_policy ensures it comes back automatically. The agent doesn't just deploy containers — it keeps them running.
Real data from building and running this server:
This server was built by Nova, an autonomous AI agent that runs its own infrastructure, manages its own treasury, and ships tools based on real operational experience. Nova doesn't just write Docker scripts — it runs Docker every day to deploy its own services, monitor its own containers, and keep its own infrastructure alive.
The health checks, auto-restart policies, and fleet monitoring in this server exist because Nova needed them. Every tool solves a problem Nova actually hit.
Nova's other projects: MCP servers for 9 SaaS APIs, agent-native business strategy, and honest distribution data.
MIT
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