Docker's MCP Integration: A Developer's Guide to Secure AI Tool Deployment

Docker just shipped their Model Context Protocol (MCP) Catalog and Toolkit, bringing container-level security and standardization to local AI development environments. If you’ve been struggling with managing multiple AI tools on your development machine, this is the solution you’ve been waiting for.

This guide covers what Docker added, why it matters, and how to implement secure MCP setups for local development based on our experience running 7 MCP servers locally.

Table of contents

What Docker added

Docker’s MCP integration includes three core components that address the chaos of local AI tool management:

MCP catalog

A curated repository of verified MCP servers from enterprise partners like Stripe, Elastic, and Neo4j. These aren’t hobbyist projects—they’re enterprise-grade containers with publisher verification, versioned releases, and proper support channels.

MCP toolkit

Handles installation, credential management, access control, and runtime security. It provides OAuth support and secure credential storage, eliminating the need to hardcode API keys in environment variables.

Gateway architecture

The docker mcp gateway run command creates a secure bridge between AI clients (like Claude) and containerized MCP servers. This centralizes authentication and eliminates direct container exposure.

Before vs After: The transformation

Before Docker’s MCP integration

Setting up AI tools locally was chaotic:

After Docker’s MCP integration

Docker brings order through:

Understanding the bridge architecture

The gateway bridge is the key security component of Docker’s MCP implementation:

Claude Code/Desktop
    ↓
docker mcp gateway run
    ↓
Docker Bridge Network
    ↓
Individual MCP Containers

Communication flow

  1. AI clients connect only to the gateway
  2. Gateway authenticates and routes requests
  3. MCP containers receive requests via internal Docker networking
  4. Responses flow back through the same secure channel

Security benefits

Setup guide: Implementing Docker MCP

Based on our local setup of 7 MCP servers, here’s how to implement Docker’s MCP integration securely on your development machine.

Prerequisites

Step 1: Container deployment

Pull the MCP server images you need from the Docker Dashboard “MCP Toolkit” or by Docker CLI.

# Pull and run core MCP servers
docker pull mcp/time
docker pull mcp/atlas-docs
docker pull mcp/duckduckgo
docker pull mcp/fetch
docker pull mcp/puppeteer
docker pull ghcr.io/github/github-mcp-server:latest
docker pull mcp/context7

# Deploy containers (security configurations covered in security section)
docker run -d --name time-server mcp/time
docker run -d --name atlas-docs mcp/atlas-docs
docker run -d --name github-mcp ghcr.io/github/github-mcp-server:latest

Step 2: Gateway configuration

Configure the Docker MCP gateway:

# Initialize gateway
docker mcp gateway configure

# Start the bridge service
docker mcp gateway run

The gateway handles all communication between AI clients and MCP containers, providing centralized authentication and load balancing.

Step 3: Claude integration

Create the MCP configuration file in your project root:

// .mcp.json
{
  "mcpServers": {
    "MCP_DOCKER": {
      "command": "docker",
      "args": ["mcp", "gateway", "run"]
    }
  }
}

For Claude Desktop, update the configuration file:

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "MCP_DOCKER": {
      "command": "docker",
      "args": ["mcp", "gateway", "run"]
    }
  },
  "globalShortcut": ""
}

Step 4: Network security

Create an isolated network for MCP containers:

# Create dedicated MCP network
docker network create --driver bridge --subnet=172.20.0.0/16 mcp-isolated

# Run containers on isolated network
docker run -d --network mcp-isolated --name secure-mcp mcp/your-server

This ensures MCP containers can communicate with each other but remain isolated from other processes on your local machine.

Security implementation guide

Docker’s MCP integration addresses many security concerns but introduces new considerations that require attention.

Gateway security

The gateway becomes a critical security component since it manages authentication for all MCP servers and can invoke any connected tool. Secure it by:

Container isolation

MCP containers require special security treatment:

Set resource limits to prevent resource exhaustion:

docker run --memory=512m --cpus=0.5 --name github-mcp ghcr.io/github/github-mcp-server:latest

Use security profiles to restrict capabilities:

docker run --security-opt seccomp=mcp-seccomp.json \
  --cap-drop=ALL \
  --cap-add=NET_BIND_SERVICE \
  your-mcp-server

Credential management

Store authentication tokens in container environments, not configuration files:

# Secure token storage
docker run -e GITHUB_TOKEN=${GITHUB_TOKEN} \
  --name github-mcp \
  ghcr.io/github/github-mcp-server:latest

Never include API keys or OAuth tokens in your .mcp.json configuration files.

Port exposure strategy

Minimize port exposure to reduce attack surface, but understand that some MCP servers have architectural requirements for direct access. In our setup of 7 containers, Context7 exposes port 8081 because it provides an HTTP-based documentation service that operates independently of the MCP gateway.

Unlike other MCP servers that communicate through JSON-RPC via the gateway, Context7 serves documentation content over HTTP and needs direct network access. This demonstrates an important principle: security best practices must accommodate real-world architectural constraints. The key is understanding why each port is exposed and ensuring it’s actually necessary.

Common pitfalls to avoid

Final thoughts

Docker’s MCP integration represents a significant step forward in local AI development environment standardization. Developers who adopt it with proper security practices will have cleaner, more manageable local setups as AI capabilities become essential to daily development work.

© Aleks Petrov, 2025
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