mcp-server-dynamic-env
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DynamicEnv MCP Server is a high-performance Kubernetes MCP server for managing DynamicEnv custom resources with real-time updates and AI-driven integration.
DynamicEnv MCP Server
A high-performance Kubernetes MCP server for managing
DynamicEnvcustom resources with real-time updates and AI-driven integration.
✨ 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.
🔍 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.
🚀 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
📄 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
🗓 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 .
📖 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 |
📁 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
🔧 Testing
# Run test suite
pytest tests/ -v
# With coverage report
pytest tests/ -v --cov=src
💪 Contributing
We welcome all contributions! Here's how to get started:
- Fork the repo
- Create a new branch
git checkout -b feature/my-awesome-feature - Commit and push your code
git commit -m "Add awesome feature" git push origin feature/my-awesome-feature - Submit a Pull Request
Please include tests and documentation updates if applicable.
💼 License
This project is licensed under the MIT License — see the file for details.
👤 Author
Yonathan Shtekel
GitHub: @yonathan-shtekel
✨ Show Support
Give this project a ⭐️ if it helped you or your team!
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.
