DynamicEnv MCP Server

!License !Python

A high-performance Kubernetes MCP server for managing DynamicEnv custom resources with real-time updates and AI-driven integration.

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✨ Overview

DynamicEnv MCP Server provides an AI-friendly API interface over DynamicEnv Kubernetes Custom Resources, enabling real-time tracking, intelligent debugging, and simplified cleanup for dynamic environments used in development, testing, and ephemeral workloads.

Built with async-first architecture and designed to integrate seamlessly with Model Context Protocol (MCP), it empowers developers and AI agents alike to manage K8s-based environments safely and efficiently.

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🔍 What is DynamicEnv?

DynamicEnv is a Kubernetes Custom Resource (CRD) that encapsulates an entire dynamic environment setup. These environments typically include:

• Multiple interdependent services
• Service-specific configurations (secrets, configs, env vars)
• Routing rules (Ingress, Service Mesh)
• Resource constraints (CPU, memory quotas)

This server enhances observability and control over these resources.

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🚀 Why MCP?

The Model Context Protocol (MCP) enables AI tools to:

• Subscribe to real-time updates
• Manage long-running async operations with progress tracking
• Work safely via validation and structured context
• Improve DevOps workflows using intelligent command suggestions

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📄 Key Features

• ✅ List & filter DynamicEnv instances by namespace, state, or deployment status
• ⏳ Live status monitoring with async updates and progress reporting
• ❌ Safe deletion of environments with dependency checks
• ⚖️ Secure K8s auth using in-cluster or kubeconfig credentials
• 🔍 Pod insights, deployment health, and optional logs
• ✨ AI-ready interface via MCP

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🗓 Quick Start

# Clone the repository
git clone https://github.com/yonathan-shtekel/mcp-server-dynamic-env.git
cd mcp-server-dynamic-env

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install dependencies
uv pip install -e .

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📖 Usage

Local Development

# Install MCP tools
pip install "mcp[cli]"

# Start the server with development tools
mcp dev server.py

# Or install and run in Claude Desktop
mcp install server.py

# Or simply run
mcp run server.py

Core CLI Tools

Tool	Description
List	View all DynamicEnvs with namespace/state filtering
Status	Inspect deployments, pods, and logs
Delete	Safely delete a DynamicEnv instance from a namespace

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📁 Project Structure

src/
  kubernetes_client.py     # Kubernetes API wrapper
  dynamicenv_service.py    # Business logic
  utils.py                 # Helpers
  models/
    dynamicenv.py          # Typed data models
tests/                     # Unit & integration tests
server.py                  # MCP-compatible API server
pyproject.toml             # Project config and dependencies

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🔧 Testing

# Run test suite
pytest tests/ -v

# With coverage report
pytest tests/ -v --cov=src

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💪 Contributing

We welcome all contributions! Here's how to get started:

1. Fork the repo
2. Create a new branch

   git checkout -b feature/my-awesome-feature
   

3. Commit and push your code

   git commit -m "Add awesome feature"
   git push origin feature/my-awesome-feature
   

4. Submit a Pull Request

Please include tests and documentation updates if applicable.

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💼 License

This project is licensed under the MIT License — see the LICENSE file for details.

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👤 Author

Yonathan Shtekel
GitHub: @yonathan-shtekel

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✨ Show Support

Give this project a ⭐️ if it helped you or your team!

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Built with ❤️ and powered by Model Context Protocol (MCP)

Development

Linting and Formatting

This project uses ruff for linting and formatting. To run the linters:

# Check for linting issues
ruff check src tests

# Auto-fix linting issues
ruff check --fix src tests

# Format code
ruff format src tests

The linting configuration is defined in pyproject.toml and includes:

• Code style (PEP 8)
• Import sorting
• Type annotation checks
• Best practices
• Code complexity

Additional Static Analysis

The project also includes other static analysis tools:

# Type checking
mypy src tests

# Security checks
bandit -r src
safety check

# Documentation coverage
interrogate src

All these checks are run automatically in CI/CD pipelines.