Rebooting Enterprise AI with MCP and Kubernetes

What It Covers

Craig Mc, CEO of Stacklock, explains how Model Context Protocol (MCP) functions as enterprise infrastructure for agentic AI systems, covering the four-component platform stack — runtime, registry, gateway, and control plane — and how Kubernetes provides the orchestration layer for scaling MCP servers across organizations.

Key Questions Answered

• •MCP Platform Stack: Enterprises need four components to operationalize MCP: a secure runtime (containerized via OCI images), a vetted registry of approved servers, a gateway providing a single endpoint across models like Claude and GPT, and a control plane for mapping servers to user groups. Building these once enables multi-model access without vendor lock-in.
• •Tool Pollution Reduction: When agents access three to four MCP servers, context windows can carry 150+ tools consuming 20,000–30,000 tokens per interaction. Replacing direct tool exposure with two proxy endpoints — find-tool and bulk-tool — reduces input token consumption by 80–90%, dramatically improving smaller model reliability from unpredictable rates to 95–97% accuracy.
• •Identity and Token Exchange: Rather than passing raw user credentials to MCP servers, enterprises should implement token exchange at the proxy layer — mapping Okta or OIDC tokens to descoped API keys or federated credentials. This pattern enforces least-privilege access, such as read-only AWS permissions, without requiring individual MCP server developers to handle authentication mechanics.
• •Agentic Concurrency as Productivity Multiplier: Stacklock's engineering team runs 5–15 simultaneous agents, each with distinct configuration and tool access, managed by one human operator. This approach produced a 60% throughput increase in a single week, enabling the team to resolve incoming issues faster than they accumulate — a pattern Craig Mc expects to transfer to non-developer knowledge workers.
• •Kubernetes for MCP Orchestration: ToolHive, an Apache 2.0 open-source project, containerizes MCP servers using OCI images, enabling file system and network endpoint restrictions per server. Kubernetes adoption for running MCP servers is growing 50% month-over-month, with millions of tool invocations now originating from Kubernetes deployments rather than developer desktops.