Introducing Docker MCP Catalog and Toolkit - Cloudy Journey

As MCPs become the backbone of agentic AI systems, the developer experience still faces key challenges. Here are some of the major hurdles: … ### Complex installations and distribution Getting started with MCP tools remains complex. Developers often have to clone repositories, wrangle conflicting dependencies in environments like Node.js or Python, and self-host local services—many of which aren’t containerized, making setup and portability even harder. On top of that, connecting MCP clients adds more friction, with each one requiring custom configuration that slows down onboarding and adoption. ### Auth and permissions fall short Many MCP tools run with full access to the host, launched via npx or uvx, with no isolation or sandboxing. Credentials are commonly passed as plaintext environment variables, exposing sensitive data and increasing the risk of leaks. Moreover, these tools often aren’t designed for scale and security. They’re missing enterprise-ready features like policy enforcement, audit logs, and standardized security.