What is Mcp Ephemeral K8s

mcp-ephemeral-k8s is a Python implementation designed to spawn ephemeral Model Context Protocol (MCP) servers using the Kubernetes API.

Use cases

Use cases include rapid deployment of testing environments, dynamic scaling of services, and temporary server instances for model training and inference in machine learning workflows.

How to use

To use mcp-ephemeral-k8s, create a new GitHub repository, set up your development environment by installing necessary dependencies, and run pre-commit hooks to ensure code quality before committing your changes.

Key features

Key features include seamless integration with Kubernetes for managing ephemeral servers, automated setup through pre-commit hooks, and a structured development environment.

Where to use

mcp-ephemeral-k8s can be used in cloud-native applications, microservices architectures, and any environment that requires dynamic server provisioning and management.

mcp-ephemeral-k8s

A Python library for spawning ephemeral Model Context Protocol (MCP) servers on Kubernetes using Server-Sent Events (SSE).

• Github: https://github.com/BobMerkus/mcp-ephemeral-k8s/
• Documentation: https://BobMerkus.github.io/mcp-ephemeral-k8s/

Features

• Supports multiple runtimes:
  • Node.js (via npx)
  • Python (via uvx)
• Works with mcp-proxy for uvx or npx runtimes
• Supports both local kubeconfig and in-cluster configuration
• Can be run as MCP server
• Can be run as FastAPI server

Usage

Running the MCP Server

uvx mcp-ephemeral-k8s

Using the Library

from mcp_ephemeral_k8s import KubernetesSessionManager, presets

with KubernetesSessionManager() as session_manager:
    mcp_server = session_manager.create_mcp_server(presets.GITHUB, wait_for_ready=True)
    print(mcp_server.sse_url)

Installation

Prerequisites

• Docker
• Kind or any Kubernetes cluster with valid kubectl configuration

Option 1: Using uvx (Recommended)

uvx mcp-ephemeral-k8s

To connect to the MCP server, use the following config:

{
  "mcp-ephemeral-k8s": {
    "url": "http://localhost:8000/sse",
    "transport": "sse"
  }
}

Option 2: As a Python Package

pip install mcp-ephemeral-k8s
mcp-ephemeral-k8s

Option 3: Using Helm Chart

To install the Helm chart, run:

helm repo add mcp-ephemeral-k8s https://BobMerkus.github.io/mcp-ephemeral-k8s/
helm repo update
helm install mcp-ephemeral-k8s mcp-ephemeral-k8s/mcp-ephemeral-k8s

To upgrade the Helm chart, run:

helm upgrade -i mcp-ephemeral-k8s mcp-ephemeral-k8s/mcp-ephemeral-k8s

To install a specific version, run:

helm install mcp-ephemeral-k8s mcp-ephemeral-k8s/mcp-ephemeral-k8s --version <replace-with-version>

To uninstall the Helm chart, run:

helm uninstall mcp-ephemeral-k8s

Option 4: From Source

1. Clone the repository

   git clone https://github.com/BobMerkus/mcp-ephemeral-k8s.git
   cd mcp-ephemeral-k8s
   

2. Set up development environment

   make install
   

3. Run pre-commit hooks

   make check
   

4. Run tests

   make test
   

5. Build Docker images

   make docker-build-local
   make docker-build-local-proxy
   

6. Load images to cluster

   kind load docker-image ghcr.io/bobmerkus/mcp-ephemeral-k8s:latest
   kind load docker-image ghcr.io/bobmerkus/mcp-ephemeral-k8s-proxy:latest
   

7. Install Helm chart

   helm upgrade -i mcp-ephemeral-k8s charts/mcp-ephemeral-k8s --set image.tag=latest
   

8. Port forward the FastAPI server

   kubectl port-forward svc/mcp-ephemeral-k8s 8000:8000
   

9. Visit the FastAPI server

   open http://localhost:8000/docs