secure, isolated environment for exploring Python development with Model Context Protocol (MCP) and Language Server Protocol (LSP). # Overview This project creates an isolated container environment that combines MCP and LSP capabilities for Python development. By leveraging the complementary strengths of both protocols, we enable LLMs to access powerful code intelligence features while maintaining strict security boundaries. # Architecture graph TD A[Host System] B[API Keys] C[Alpine Container] D[Run-Python MCP] E[MultilspyLSP] F[Python LSP Server] G[Client Tools] H[Python Algorithms] A --> B A --> C B --> D B --> E C --> D C --> E C --> F C --> G C --> H D --> H E --> F F --> H G --> D G --> E # Security Model The project implements a principle of least access architecture: - Container isolation from host system - Non-root user execution within container - Restricted port exposure (bound to localhost only) - Secure secrets management via GitHub Secrets - Resource limits on container (memory, CPU) - Input validation and sanitization - Clear security domain boundaries between components See <./SECURITY.md> for comprehensive security guidelines and best practices. # Core Components - **Pydantic Run-Python**: Executes Python code via MCP - **MultilspyLSP**: Bridges LSP capabilities to MCP - **Python LSP Server**: Provides code intelligence (completion, analysis, diagnostics) - **Client Interfaces**: Multiple access methods with the same security model # Integration Points

Component	Protocol	Function
Run-Python	MCP	Code execution and output capture
MultilspyLSP	MCP+LSP	Code intelligence bridge
Python LSP	LSP	Static analysis and completion
Claude Code	-	AI-assisted analysis and exploration

# Getting Started 1. Initial setup: # Create required directories make dirs # Generate configuration files from org sources make tangle # Set up GitHub CLI authentication for secrets gh auth login 1. Set up secrets management: # Run the secrets setup script ./scripts/setup_secrets.sh # Or manually update the GitHub secrets with your actual keys gh secret edit GH_PAT gh secret edit ANTHROPIC_API_KEY 1. Build and run the container: # Build the Docker/Podman image make build # Run the container (automatically retrieves secrets) make run 1. Test the environment: # Verify MCP server connectivity make test # Try analyzing an algorithm (after creating one) make analyze ALGO=fibonacci # Command Reference Run `make` or `gmake help` for a full list of available commands. Key commands for getting started: - `make build` - Build the Docker/Podman image - `make run` - Start container with mounted volumes - `make test` - Verify MCP server connectivity - `make analyze ALGO=fibonacci` - Analyze algorithm via MCP ## Using MCP Run Python Directly You can interact with the MCP Run Python server directly

#isolation

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