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StacklokLabs/mkp

by Various

MKP is a Model Context Protocol (MCP) server for Kubernetes

S

MCP

StacklokLabs/mkp

Added 1 June 2026

Overview

MKP is a Model Context Protocol (MCP) server for Kubernetes. It exposes Kubernetes resources and operations through the MCP interface, enabling AI agents to interact with clusters. Built in Go, it provides a standardized way for LLMs to query and manage Kubernetes objects.

Best for

Best for
Developers building AI agents that need programmatic access to Kubernetes clusters

Use cases

  • Query cluster state and resource configurations via natural language
  • Automate routine Kubernetes operations through AI agent integrations
  • Enable AI-assisted debugging and troubleshooting of Kubernetes workloads

How to use

Tools exposed

  • get_resource
  • list_resources
  • apply_resource

Notes

MKP is a Model Context Protocol (MCP) server for Kubernetes. It exposes Kubernetes resources and operations through the MCP interface, enabling AI agents to interact with clusters. Built in Go, it provides a standardized way for LLMs to query and manage Kubernetes objects.

57 stars on GitHub. Last updated 2026-05-29. Licensed Apache-2.0.

Use cases

  • Query cluster state and resource configurations via natural language
  • Automate routine Kubernetes operations through AI agent integrations
  • Enable AI-assisted debugging and troubleshooting of Kubernetes workloads

Pros

  • Leverages the MCP standard for broad AI tool compatibility
  • Lightweight Go implementation with minimal dependencies
  • Directly addresses Kubernetes operational tasks without custom scripting

Cons

  • Small community and limited adoption (57 stars)
  • Requires MCP-compatible AI clients to be useful
  • May lack advanced Kubernetes features or edge-case handling

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Leverages the MCP standard for broad AI tool compatibility
  • Lightweight Go implementation with minimal dependencies
  • Directly addresses Kubernetes operational tasks without custom scripting

Cons

  • Small community and limited adoption (57 stars)
  • Requires MCP-compatible AI clients to be useful
  • May lack advanced Kubernetes features or edge-case handling
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