Most discussions about Model Context Protocol infrastructure ask how to govern thousands of AI tools and monitor which MCP servers are running. This question is table stakes but undershoots the possibilities. A better question is how we can unleash MCP to drive developer creativity from a trusted foundation.

The first question produces a phone book of curated, controlled, static resources. The second points toward an AI playground where agents and developers interact and learn from each other. What if private catalogs of MCP servers become composable playlists that encourage mixing, reshaping, and myriad combinations of tool calls? This requires treating MCP catalogs as OCI artifacts, not databases.

Cloud-native computing created feedback loops where infrastructure became code, deployments became declarative, and operational knowledge became shareable artifacts. MCP catalogs need to follow the same path. OCI artifacts, immutable versioning, and container-native workflows provide the model because they represent a well-understood approach that balances trust with creative evolution.

Trust Boundaries That Expand and Learn

iTunes provided a store. Spotify provided a store plus algorithmic discovery, playlist sharing, and taste profiles that improved over time. Private MCP catalogs can enable the same evolution. Today, this means curated, verified collections. Tomorrow, this becomes the foundation for self-improving discovery systems.

Tens of thousands of MCP servers are scattered across GitHub, registries, and forums. Community registries like mcp.so, Smithery, Glama, and PulseMCP are attempting to organize this ecosystem, but provenance remains unclear and quality varies wildly. Private catalogs with tighter access controls offer centralized discovery, enhanced security through vetted servers, and visibility into which tools developers actually use. Organizations can build curated subsets of approved servers, add proprietary internal servers, and selectively import from community registries. This solves the phone book problem.

When Output Becomes Input

The real opportunity is when the work agents do creates shareable artifacts plus organizational learning automatically. Your agent faces a complex problem analyzing customer churn across three data sources. The MCP gateway then constructs a profile capturing the tools, API keys, sequence of operations, and documentation about what worked. That profile becomes an OCI artifact in your registry.

Next month, another team faces a similar problem. Their agent pulls your profile as a starting point, adapts it, and pushes a refined version. The customer success team creates a churn profile combining data warehouse connectors, visualization tools, and notification servers. The sales team imports that profile, adds CRM connectors, and uses it to strategize on renewals. They publish their enhanced version back to the catalog. Teams stop rebuilding identical solutions and instead reuse or remix. Knowledge is captured, shared, and refined.

Why OCI Makes This Possible

Treating catalogs as immutable OCI artifacts lets agents pin to versions or profiles. Your production agents use catalog v2.3 while QA uses v2.4, and they do not drift. Without this, Agent A mysteriously fails because the database connector it relied on got silently updated with breaking changes. Audit trails become straightforward. You can prove which tools were available when incident X occurred. OCI-based catalogs are the only approach that makes catalogs and agents first-class infrastructure fully addressable with GitOps tooling.

OCI with containers delivers two benefits that matter for MCP. First, containers provide hermetic but customizable and context-rich security boundaries. The MCP server runs in a sandboxed container with explicit network policies, filesystem isolation, and resource limits. Secret injection happens through standard mechanisms with no credentials in prompts. This is key if MCP servers execute arbitrary code or have filesystem access.

Second, containers and the associated OCI versioning appends reusable governance tooling in just the right way, matching other governance tooling in your general container stack and workflow. Because catalogs are OCI artifacts, image scanning works the same. Signing and provenance use Cosign on catalogs just like images. Harbor, Artifactory, and other registries already have sophisticated access controls. Policy enforcement through OPA applies to catalog usage as it does to container deployments. Your FedRAMP-approved container registry handles MCP catalogs too. Your security team does not need to learn new tools.

From Phone Books and iTunes to Intelligent Platforms and Spotify

Organizations can evolve to dynamic discovery within trust boundaries. An MCP gateway allows the agent to query the catalog at runtime, select the appropriate tool, and instantiate only what it needs. With Docker’s Dynamic MCPs in the MCP Gateway, the agent can also call built-in tools like mcp-find and mcp-add to search curated catalogs, pull and start new MCP servers on demand, and drop them when they are no longer needed, instead of hard-coding tool lists and configs. Dynamic MCPs keep unused tools out of the model’s context, reduce token bloat, and let agents assemble just-in-time workflows from a much larger pool of MCP servers. The longer-term vision goes further. The gateway captures semantic intelligence around how users interact with MCPs, learns which tools combine effectively, and suggests relevant servers based on how similar problems were previously solved.

The longer-term vision goes further. The gateway captures semantic intelligence around how users interact with MCPs, learns which tools combine effectively, and suggests relevant servers based on how similar problems were previously solved. Teams both learn from and add to this knowledge feedback loop, Private catalog users discover new MCPs, mix MCPs in useful ways, and develop new ways of doing things,inspired by their own thoughts and by suggestions from the MCP gateway. This process also provides live reinforcement learning, imparting wisdom and context to the system that can benefit and everyone using the gateway. This is organizational memory as infrastructure, emergent from actual agent work that blends human and machine intelligence in unlimited ways..

The container-native approach using private catalogs, dynamic MCP for runtime discovery, profiles as OCI artifacts, and sandboxed execution builds a composable, secure foundation for this future AI playground. How can we unleash MCP to drive developer creativity from a trusted foundation? Treat it like we treated containers but afford it the privileges that AI deserves as agentic, intelligent systems. Private MCP catalogs endowed with semantic intelligence and context understanding, built atop OCI versioned infrastructure, running in safe agent sandboxes, are the first step toward that vision.