The cli-mcp-server is a secure MCP server designed to bring the power of command-line tools to your AI models in a safe and controlled manner. As a server within the MCP ecosystem, it allows AI models to execute pre-approved command-line operations, enhancing their ability to interact with the underlying system.

This server boasts robust security features, including command whitelisting, path validation, and protection against shell injection, addressing critical security concerns for developers. It seamlessly integrates with AI models through the MCP client-server architecture, receiving commands and returning results. The core value lies in enabling AI models to leverage existing command-line utilities without compromising system security.

Configuration is straightforward using environment variables, and the server provides detailed error reporting for easy debugging. By using cli-mcp-server, developers can confidently extend their AI models' capabilities with secure command-line access. It supports standard input/output for communication.

Secure Command Execution

The cli-mcp-server's core function is to execute command-line operations securely. It achieves this by strictly validating all commands against a whitelist defined in the server's configuration. This whitelist specifies which commands are permitted to run, preventing the execution of arbitrary or potentially harmful commands. The server also validates any flags passed to these commands, ensuring that only approved options are used. This dual-layered validation process significantly reduces the risk of security breaches and unauthorized system access. For example, an AI model might need to list files in a specific directory but should not be allowed to execute system-level commands. The cli-mcp-server allows the ls command with specific flags like -l or -a, while blocking any attempt to use commands like rm or sudo. This feature is crucial for maintaining the integrity and security of the underlying system while providing AI models with necessary functionality.

Path Traversal Prevention

This feature is designed to prevent malicious actors from accessing files and directories outside of the intended working directory. The cli-mcp-server meticulously validates all file paths provided in commands to ensure they fall within the ALLOWED_DIR specified in the server's configuration. This validation process includes normalization to resolve symbolic links and prevent path traversal attacks using techniques like ".." in the path. By enforcing strict path controls, the server ensures that AI models can only interact with authorized files and directories, mitigating the risk of data breaches or unauthorized modifications. For instance, if ALLOWED_DIR is set to /data, any attempt to access /etc/passwd would be blocked, even if the allowed command is cat. This feature is essential for maintaining data confidentiality and preventing unauthorized access to sensitive system resources.

Configurable Whitelisting

The cli-mcp-server offers highly configurable command and flag whitelisting, providing flexibility while maintaining robust security. Administrators can define precisely which commands and flags are allowed, or use the 'all' option to permit any command or flag. This allows for tailored security policies based on the specific needs of the AI model and the environment it operates in. The whitelisting feature prevents unauthorized command execution and limits the potential attack surface. For example, in a data processing pipeline, the server might be configured to allow only grep, sed, and awk commands with specific flags, effectively restricting the AI model's ability to perform potentially harmful operations. This level of control is crucial for balancing functionality and security in AI-driven applications.

Detailed Error Reporting

The cli-mcp-server provides comprehensive error reporting, enabling developers to quickly identify and resolve issues. When a command fails to execute due to security violations, timeouts, or other errors, the server returns detailed error messages that specify the cause of the failure. These messages include information about the specific security rule that was violated, the command that timed out, or the nature of the execution failure. This detailed feedback allows developers to diagnose and fix problems efficiently, reducing debugging time and improving the overall reliability of the system. For example, if a command is blocked due to a missing whitelist entry, the error message will clearly indicate that the command is not allowed, prompting the developer to update the server's configuration. This feature is invaluable for maintaining a stable and secure environment for AI model execution.