To celebrate International Women’s Day, we sat down with Cecilia Liu, Senior Product Manager at Docker, for three questions about the vision and strategy behind Docker’s MCP solutions. From shaping product direction to driving AI innovation, Cecilia plays a key role in defining how Docker enables secure, scalable AI tooling.
Cecilia leads product management for Docker’s MCP Catalog and Toolkit, our solution for running MCP servers securely and at scale through containerization. She drives Docker’s AI strategy across both enterprise and developer ecosystems, helping organizations deploy MCP infrastructure with confidence while empowering individual developers to seamlessly discover, integrate, and use MCP in their workflows. With a technical background in AI frameworks and an MBA from NYU Stern, Cecilia bridges the worlds of AI infrastructure and developer tools, turning complex challenges into practical, developer-first solutions.
What products are you responsible for?
I own Docker’s MCP solution. At its core, it’s about solving the problems that anyone working with MCP runs into: how do you find the right MCP servers, how do you actually use them without a steep learning curve, and how do you deploy and manage them reliably across a team or organization.
How does Docker’s MCP solution benefit developers and enterprise customers?
Dev productivity is where my heart is. I want to build something that meaningfully helps developers at every stage of their cycle — and that’s exactly how I think about Docker’s MCP solution.
For end-user developers and vibe coders, the goal is simple: you shouldn’t need to understand the underlying infrastructure to get value from MCP. As long as you’re working with AI, we make it easy to discover, configure, and start using MCP servers without any of the usual setup headaches. One thing I kept hearing in user feedback was that people couldn’t even tell if their setup was actually working. That pushed us to ship in-product setup instructions that walk you through not just configuration, but how to verify everything is running correctly. It sounds small, but it made a real difference.
For developers building MCP servers and integrating them into agents, I’m focused on giving them the right creation and testing tools so they can ship faster and with more confidence. That’s a big part of where we’re headed.
And for security and enterprise admins, we’re solving real deployment pain, making it faster and cheaper to roll out and manage MCP across an entire organization. Custom catalogs, role-based access controls, audit logging, policy enforcement. The goal is to give teams the visibility and control they need to adopt AI tooling confidently at scale.
Customers love us for all of the above, and there’s one more thing that ties it together: the security that comes built-in with Docker. That trust doesn’t happen overnight, and it’s something we take seriously across everything we ship.
What are you excited about when it comes to the future of MCP?
What excites me most is honestly the pace of change itself. The AI landscape is shifting constantly, and with every new tool that makes AI more powerful, there’s a whole new set of developers who need a way to actually use it productively. That’s a massive opportunity.
MCP is where that’s happening right now, and the adoption we’re seeing tells me the need is real. But what gets me out of bed is knowing the problems we’re solving: discoverability, usability, deployment. They are all going to matter just as much for whatever comes next. We’re not just building for today’s tools. We’re building the foundation that developers will reach for every time something new emerges.
Cecilia is speaking about scaling MCP for enterprises at the MCP Dev Summit in NYC on 3rd of April, 2026. If you’re attending, be sure to stop by Docker’s booth (D/P9).
Learn more
Explore Docker’s MCP Catalog and Toolkit on our website.
Dive into our documentation to get started quickly.
Ready to go hands-on? Open Docker Desktop or the CLI and start using MCP to streamline and automate your development workflows.
Quelle: https://blog.docker.com/feed/
