Kubernetes Claude MCP Kubernetes Claude MCP

Model Context Protocol

Learn about the Model Context Protocol (MCP) and how it powers AI-driven analysis of Kubernetes and GitOps workflows.

Model Context Protocol

The Model Context Protocol (MCP) is the core technology behind the Kubernetes Claude MCP server. It enables the collection, correlation, and presentation of rich contextual information to Claude AI, allowing for deep analysis and troubleshooting of complex Kubernetes environments.

What is MCP?

MCP is a framework for providing structured context to large language models (LLMs) like Claude. It solves a fundamental challenge when using AI to analyze complex systems: how to collect and structure all the relevant information about a system so that an AI can understand the complete picture.

In the context of Kubernetes and GitOps:

  • Complete Context: MCP gathers comprehensive information about resources, their relationships, history, and current state.
  • Cross-System Correlation: It correlates information from Kubernetes, ArgoCD, GitLab, and other systems.
  • Intelligent Filtering: It filters and prioritizes information to focus on what’s most relevant.
  • Structured Formatting: It presents information in a way that maximizes Claude’s understanding.

Core Components of MCP

The MCP framework consists of several key components:

1. Context Collection

The first step of MCP is gathering comprehensive information about the system being analyzed. For Kubernetes environments, this includes:

  • Resource Definitions: The complete YAML/JSON specifications of resources
  • Resource Status: Current runtime status information
  • Events: Related Kubernetes events
  • Logs: Container logs for relevant pods
  • Relationships: Parent-child relationships between resources
  • History: Deployment history, changes, and previous states
  • GitOps Context: ArgoCD sync status, GitLab commits, pipelines

2. Context Correlation

Once data is collected, MCP correlates information across different systems:

  • Resource to Git: Which Git repository, branch, and files define a resource
  • Resource to CI/CD: Which pipelines deployed a resource
  • Resource to Owners: Which teams or individuals own a resource
  • Dependencies: How resources depend on each other
  • Change Impact: How changes in one system affect others

3. Context Formatting

MCP formats the correlated information in a standardized structure:

  • Hierarchical Organization: Information is organized in a logical hierarchy
  • Relevance Sorting: Most important information is presented first
  • Cross-References: Clear references between related pieces of information
  • Compact Representation: Information is presented efficiently to maximize context window usage

4. Context Presentation

Finally, MCP presents the formatted context to Claude for analysis:

  • System Prompt: Instructs Claude on how to interpret the context
  • User Query: Focuses Claude’s analysis on specific questions or issues
  • Analysis Parameters: Controls the depth, breadth, and style of analysis

MCP in Action

Here’s a simplified view of how MCP works when troubleshooting a Kubernetes deployment:

  1. User Query: “Why is my deployment not scaling?”
  2. Context Collection: MCP gathers information about the deployment, related pods, events, logs, node resources, and GitOps configurations.
  3. Context Correlation: MCP connects the deployment to its ArgoCD application and recent GitLab commits.
  4. Context Formatting: The information is structured in a hierarchical format that prioritizes scaling-related details.
  5. Claude Analysis: Claude analyzes the context and identifies that the deployment can’t scale because of resource constraints.
  6. Response: The user receives a detailed explanation and recommendations.

Protocol Architecture

The MCP implementation consists of several key components:

1. Collectors

Collectors are responsible for gathering information from different sources:

  • Kubernetes Collector: Gathers resource definitions, status, and events
  • ArgoCD Collector: Gathers application definitions, sync status, and history
  • GitLab Collector: Gathers repository information, commits, and pipelines
  • Log Collector: Gathers container logs and application logs

2. Correlators

Correlators connect information across different systems:

  • GitOps Correlator: Connects Kubernetes resources to their Git definitions
  • Deployment Correlator: Connects resources to their deployment pipelines
  • Issue Correlator: Connects observed issues to their potential causes
  • Resource Correlator: Connects resources to their related resources

3. Context Manager

The Context Manager is responsible for organizing and formatting the context:

  • Context Selection: Determines what information to include
  • Context Prioritization: Prioritizes the most relevant information
  • Context Formatting: Formats the information for maximum effectiveness
  • Context Truncation: Ensures the context fits within Claude’s context window

4. Protocol Handler

The Protocol Handler handles the interaction with Claude:

  • Prompt Generation: Creates effective system and user prompts
  • Response Processing: Processes and formats Claude’s responses
  • Follow-up Management: Handles follow-up queries and clarifications

Example MCP Context

Here’s a simplified example of how MCP formats context for Claude:

# Kubernetes Resource: Deployment/my-app
Namespace: default
API Version: apps/v1

## Specification
Replicas: 5
Strategy: RollingUpdate
Selector: app=my-app
Template:
  ...truncated for brevity...

## Status
Available Replicas: 3
Ready Replicas: 3
Updated Replicas: 3
Conditions:
- Type: Available, Status: True
- Type: Progressing, Status: True

## Recent Events
1. [Warning] FailedCreate: pods "my-app-7b9d7f8d9-" failed to fit in any node
2. [Normal] ScalingReplicaSet: Scaled up replica set my-app-7b9d7f8d9 to 5
3. [Warning] FailedScheduling: 0/3 nodes are available: insufficient cpu

## ArgoCD Application
Name: my-app
Sync Status: Synced
Health Status: Degraded
Source: https://github.com/myorg/myrepo.git
Path: applications/my-app
Target Revision: main

## Recent GitLab Commits
1. [2025-03-01T10:15:30Z] 7a8b9c0d: Increase replicas from 3 to 5 (John Smith)
2. [2025-02-28T15:45:20Z] 1b2c3d4e: Update resource requests (Jane Doe)

## Node Resources
Total CPU Capacity: 12 cores
Used CPU: 10.5 cores
Available CPU: 1.5 cores

This formatted context makes it easy for Claude to understand the complete picture and identify that the deployment can’t scale to 5 replicas because of insufficient CPU resources.

Benefits of MCP

Using the Model Context Protocol provides several key benefits:

  1. Complete Understanding: Claude gets a holistic view of your environment.
  2. Deeper Analysis: With more context, Claude can provide more accurate and insightful analysis.
  3. Cross-System Correlation: Issues that span multiple systems are easier to identify.
  4. Efficient Context Usage: Structured information maximizes the use of Claude’s context window.
  5. Consistent Analysis: Standardized context leads to more consistent analysis over time.

Extending MCP

The Model Context Protocol is designed to be extensible. You can add support for additional systems and information sources:

  1. Custom Collectors: Implement collectors for your specific systems.
  2. Custom Correlators: Create correlators for your organization’s workflows.
  3. Context Templates: Define custom context templates for your use cases.
  4. Prompt Templates: Customize prompts for your specific needs.

For more information on extending MCP, see the Custom Integrations guide.

Next Steps

Now that you understand the Model Context Protocol, you can:

  1. Explore GitOps Integration to learn how MCP connects with ArgoCD and GitLab.
  2. Try Troubleshooting Resources to see MCP in action.
  3. Review the API Reference to learn how to interact with the MCP server.