Docker Desktop 4.43 Expanded Model Runner, Reimagined MCP Catalog, MCP Server Submissions, and Smarter Gordon

just rolled out a set of powerful updates that simplify how developers run, manage, and secure AI m

just rolled out a set of powerful updates that simplify how developers run, manage, and secure AI models and MCP tools.  Model Runner now includes better model management, expanded OpenAI API compatibility, and fine-grained controls over runtime behavior. The improved MCP Catalog makes it easier to discover and use MCP servers, and now supports submitting your own MCP servers! Meanwhile, the MCP Toolkit streamlines integration with VS Code and GitHub, including built-in OAuth support for secure authentication. Gordon, Docker’s AI agent, now supports multi-threaded conversations with faster, more accurate responses. And with the new Compose Bridge, you can convert local compose.yaml files into Kubernetes configuration in a single command.  Together, these updates streamline the process of building agentic AI apps and offer a preview of Docker’s ongoing efforts to make it easier to move from local development to production. This release includes improvements to the user interface of the Docker Model Runner, the inference APIs, and the inference engine under the hood. Starting with the user interface, developers can now inspect models (including those already pulled from Docker Hub and those available remotely in the ) via model cards available directly in Docker Desktop. Below is a screenshot of what the model cards look like: In addition to the GUI changes, the command adds three new subcommands to  help developers inspect, monitor, and manage models more effectively: For WSL2 users who enable Docker Desktop integration, all of the commands are also now available from their WSL2 distros, making it easier to work with models without changing your Linux-based workflow. On the API side, Model Runner now offers additional OpenAI API compatibility and configurability. Specifically, tools are now supported with , making agents built on Docker Model Runner more dynamic and responsive. Model Runner’s API endpoints now support calls for better compatibility with existing tooling. Finally, developers can now configure CORS origins in the Model Runner settings pane, offering better compatibility and control over security.  For developers who need fine-grained control over model behavior, we’re also introducing the ability to set a model’s context size and even the runtime flags for the inference engine via Docker Compose, for example: In this example, we’re using the (optional) and parameters to control the behavior of the inference engine underneath. In this case, the associated runtime is the default ( ), and you can find a list of flags . Certain flags may override the stable default configuration that we ship with Docker Desktop, but we want users to have full control over the inference backend. It’s also worth noting that a particular model architecture may limit the maximum context size. You can find information about maximum context lengths on the . Under the hood, we’ve focused on improving stability and usability. We now have better error reporting in the event that an inference process crashes, along with more aggressive eviction of crashed engine processes. We’ve also enhanced the Docker CE Model Runner experience with better handling of concurrent usage and more robust support for model providers in Compose on Docker CE. now features an improved experience, making it easier to search, discover, and identify the right MCP servers for your workflows. You can still access the catalog through Docker Hub or directly from the MCP Toolkit in Docker Desktop, and now, it’s also available via a for even faster access.  The MCP Catalog currently includes over 100 verified, containerized tools, with hundreds more on the way. Unlike traditional npx or uvx workflows that execute code directly on your host, every MCP server in the catalog runs inside an isolated Docker container. Each one includes cryptographic signatures, a Software Bill of Materials (SBOM), and provenance attestations.  This approach eliminates the risks of running unverified code and ensures consistent, reproducible environments across platforms. Whether you need database connectors, API integrations, or development tools, the MCP Catalog provides a trusted, scalable foundation for AI-powered development workflows that move the entire ecosystem away from risky execution patterns toward production-ready, containerized solutions. We’re launching a new submission process, giving developers flexible options to contribute by following the process .  Developers can choose between two options: Docker-Built and Community-Built servers.  When you see “Built by Docker,” you’re getting our complete security treatment. We control the entire build pipeline, providing cryptographic signatures, SBOMs, provenance attestations, and continuous vulnerability scanning. These servers are packaged as Docker images by their developers. While we don’t control their build process, they still benefit from container isolation, which is a massive security improvement over direct execution. Docker-built servers demonstrate the gold standard for security, while community-built servers ensure we can scale rapidly to meet developer demand. Developers can change their mind after submitting a community-built server and opt to resubmit it as a Docker-built server.  Get your MCP server featured in the Docker MCP Catalog today and reach over 20 million developers. Learn more about our new MCP Catalog in our announcement and get insights on on building, running, and testing MCP servers.  in building the largest library of secure, containerized MCP servers! . Many MCP servers’ credentials are passed as plaintext environment variables, exposing sensitive data and increasing the risk of leaks. The MCP Toolkit eliminates that risk with secure credential storage, allowing clients to authenticate with MCP servers and third-party services without hardcoding secrets. We’re taking it a step further with OAuth support, starting with the most widely used developer tool, GitHub. This will make it even easier to integrate secure authentication into your development workflow. To set up your GitHub MCP server, go to the OAuth tab, connect your GitHub account, enable the server, and authorize OAuth for secure authentication. The MCP Toolkit allows you to connect MCP servers to any MCP client, with one-click connection to popular ones such as Claude and Cursor. We are also making it easier for developers to connect to VSCode with the docker mcp client connect vscode command. When run in your project’s root folder, it creates an mcp.json configuration file in your .vscode folder.  . Additionally, you can also configure the MCP Toolkit as a global MCP server available to VSCode by adding the following config to your user settings. Check out for more details. Once connected, you can leverage GitHub Copilot in agent mode with full access to your repositories, issues, and pull requests. Docker’s AI Agent Gordon just got a major upgrade: multi-threaded conversation support. You can now run multiple distinct conversations in parallel and switch between topics like debugging a container issue in one thread and refining a Docker Compose setup in another, without losing context. Gordon keeps each thread organized, so you can pick up any conversation exactly where you left off. Gordon’s new multi-threaded capabilities work hand-in-hand with MCP tools, creating a powerful boost for your development workflow. Use Gordon alongside your favorite MCP tools to get contextual help while keeping conversations organized by task. No more losing focus to context switching! . We’ve also rolled out major performance upgrades, Gordon now responds 5x faster and delivers more accurate, context-aware answers. With improved understanding of Docker-specific commands, configurations, and troubleshooting scenarios, Gordon is smarter and more helpful than ever! We know that developers love Docker Compose for managing local environments—it’s simple and easy to understand. We’re excited to introduce Compose Bridge to Docker Desktop. This new powerful feature helps you transform your local compose.yaml into Kubernetes configuration with a single command. Compose Bridge gives you a streamlined, flexible way to bring your Compose application to Kubernetes. With smart defaults and options for customization, it’s designed to support both simple setups and complex microservice architectures. All it takes is: And just like that, Compose Bridge generates the following Kubernetes resources from your Compose file: This approach replicates your local dev environment in Kubernetes quickly and accurately, so you can test in production-like conditions, faster. Need something more customized? Compose Bridge supports advanced transformation options so you can tweak how services are mapped or tailor the resulting configuration to your infrastructure. And we’re not stopping here—upcoming releases will allow Compose Bridge to generate Kubernetes config based on your existing cluster setup, helping teams align development with production without rewriting manifests from scratch. You can start using Compose Bridge today: Docker Desktop 4.43 introduces practical updates for developers building at the intersection of AI and cloud-native apps. Whether you’re running local models, finding and running secure MCP servers, using Gordon for multi-threaded AI assistance, or converting Compose files to Kubernetes, this release cuts down on complexity so you can focus on shipping. From agentic AI projects to scaling workflows from local to production, you’ll get more control, smoother integration, and fewer manual steps throughout.