Container-MCP offers a sandboxed, container-based implementation of the Model Context Protocol (MCP) enabling secure code execution, command running, file access, and web operations within isolated environments, ensuring security and resource management.
Added on:
Created by:
Apr 22 2025
Container-MCP offers a sandboxed, container-based implementation of the Model Context Protocol (MCP) enabling secure code execution, command running, file access, and web operations within isolated environments, ensuring security and resource management.
Added on:
Created by:
Apr 22 2025
Martin
Featured
What is Container-MCP?
Container-MCP is a secure, containerized system that implements the MCP protocol, allowing large language models and AI systems to safely execute tools such as code execution, command running, file handling, and web operations. It leverages Podman or Docker containers with multiple security layers including AppArmor and Firejail, enforcing resource limits and preventing malicious activities. It provides domain-specific managers like BashManager, PythonManager, FileManager, and WebManager for secure interaction with system components and web resources. The system is highly configurable, supporting environment variables for security policies, resource constraints, and extension restrictions, making it suitable for AI-driven applications requiring safe and isolated environment execution.
Who will use Container-MCP?
- AI developers
- ML researchers
- System administrators
- AI system integrators
How to use the Container-MCP?
- Step1: Set up the environment using the provided installation scripts or manual steps
- Step2: Build and run the container with Docker or Podman
- Step3: Configure environment variables for security and resource limits
- Step4: Connect to the MCP server via client implementations
- Step5: Use MCP client to discover and execute available tools
Container-MCP's Core Features & Benefits
The Core Features
- System command execution
- Python code execution
- File reading, writing, listing, deleting
- Web searching and scraping
- Secure web browsing
- Resource and security controls
- Tool discovery and management
The Benefits
- High security through containerization and sandboxing
- Resource management and restriction for safe operation
- Support for multiple tool types with secure APIs
- Flexible configuration for various security policies
- Isolation to protect host system integrity
Container-MCP's Main Use Cases & Applications
- AI system tool integration for code execution and web scraping
- Secure sandboxed environment for ML experimentations
- Automated workflows requiring safe file and code management
FAQs of Container-MCP
Developer
- 54rt1n
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