Docker MCP Toolkit: Hassle-Free Local Agentic AI with MCP Servers

Have recently been looking at a number of MCP servers to assist me in AI development, looking at how I can run these locally – without much configuration overhead.. I just want to get going!

Came across Docker MCP Gateway & Toolkit and certainly feel its worth a blog post! Both are great, work side-by-side with ease!

What is an MCP server?

At its core, an MCP Server (Model Context Protocol server) acts as the bridge between your AI agent and the rest of your tech stack. Think of it as the interpreter that enables your agent to interact with APIs, cloud services, SaaS platforms, infrastructure tools, and much more.

If you’re working with agentic AI – whether it’s GitHub Copilot, Claude Code, Cursor, or your own custom bots – MCP servers empower these agents to:

• Pull live data (like open GitHub issues or Jira tickets)
• Manage cloud infrastructure (auto-provision AWS, Azure, GCP resources)
• Automate routine tasks in everyday apps (send Slack messages, update Trello boards)
• Coordinate complex workflows across multiple platforms

In short, MCP servers transform AI agents from passive suggestion engines into active, hands-on automation tools.

What MCP Servers Are Available?

You are probably wondering – what sort of MCP servers are available? In short lots!

• GitHub MCP Server: Lets AI tools read code, manage issues, automate pull requests—all via simple prompts.
• HashiCorp/Terraform MCP Server: Integrates AI with Infrastructure-as-Code (IaC), automatically coordinating Terraform Registry APIs for cloud builds.
• Azure & BlueBridge: Check out open source projects like AKS MCP, BlueBridge, and the growing list at modelcontextprotocol/servers
• Jira, Slack, and more: Automate ticketing, messaging, and business operations—all through your agent.

Thats only a select few of the 100s of available MCP servers! For the latest additions, check the modelcontextprotocol/servers GitHub repo or browse the MCP Catalog within the Docker MCP Toolkit. The selection covers everything from major cloud providers to productivity apps.

Running MCP servers locally

This is where Docker MCP Toolkit and MCP Gateway shine. Forget about wrestling with Python versions or sharing secrets in plain text. With these, running your favourite MCP server is almost fun.

Running MCP servers on your own machine changes the game for development and testing. Here’s why it’s worthwhile:

• No cloud dependencies: Prototype and debug rapidly, without burning through cloud credits.
• Stronger security: Secrets and credentials stay local – no more scattering sensitive info across devices.
• Quick iteration: Experiment with new workflows, servers, or integrations before pushing to production.
• Consistent environments: Containers let you wipe the slate clean or replicate setups with ease.

What is the Docker MCP toolkit?

Think of it as a friendly gateway – it lets you set up, manage, and run MCP servers right from Docker Desktop. Want to have your AI agent fetch Jira tickets or send messages to Slack? The Toolkit makes this as easy as clicking a button.

Right inside Docker Desktop, it will let you:

• Install with a click – no manual setup or editing config files
• Browse and launch servers – find the ones you want in a searchable catalog
• Connect instantly – support for GitHub Copilot, Claude Code, Cursor, and more out of the box
• Stay secure – everything runs in containers, isolated from the rest of your system
• Aggregate your MCP servers – manage everything in one tidy spot
• Discover hundreds of MCP servers – You can also upload/create your own

Docker MCP Toolkit Highlights

• Simple installation: Find, launch, and manage MCP servers from a searchable catalog
• No manual setup: Skip the config file gymnastics and secret hunting
• Instant connections: Built-in support for Copilot, Claude, Cursor, and custom agents
• Secure isolation: Each server runs in its own container
• Unified management: Handle multiple MCP servers from a single dashboard
• Customisable catalog: Discover, upload, and create your own MCP servers

If you appreciate tools that let you get straight to work, this philosophy will resonate.

How does it all work?

Using a MCP Client & server:

• MCP Clients: These are AI apps like GitHub Copilot or Claude Code. They make requests, like “fetch my latest GitHub issues.”
• MCP Servers: These are smart connectors, packaged as containers, that actually perform the tasks. So if GitHub Copilot asks, “Find Jira tickets assigned to me,” an installed MCP server for Jira springs into action.

Actionable Steps

If you’re building agentic AI, orchestrating workflows, or just want a simple way to experiment with MCP servers locally, here’s how to dive in:

1. Install Docker Desktop and add the MCP Toolkit extension
2. Explore the MCP Catalog – try out servers for GitHub, Jira, Terraform etc
3. Configure your agent to use the local MCP Gateway endpoint
4. Start experimenting: Automate workflows, fetch live data, and build your own integrations

Wrapping up

The future of agentic AI depends on solid, secure, and easy-to-use building blocks. The Docker MCP Catalog and Toolkit lays a powerful foundation – not just for developers, but for whole teams and enterprises aiming to innovate faster and safer.

Curious to see what you can build? Take a look at the Docker MCP Catalog and the MCP Toolkit in Docker Desktop. There’s plenty of room for creativity and new workflows.