Modern Cli MCP
An MCP server that provides 107 modern command-line tools, offering JSON-RPC structured access to file systems, Git platforms, containers, Kubernetes, and data conversion tools for AI/LLM agents.
rating : 2 points
downloads : 6.1K
What is the Modern CLI MCP Server?
The Modern CLI MCP Server is a Model Context Protocol (MCP) server that encapsulates 107 modern command-line tools into an AI-friendly interface. Through this server, AI assistants (such as Claude) can directly use these tools to perform various tasks, including file browsing, code searching, Git operations, container management, Kubernetes deployment, etc. The outputs of all tools are automatically converted into structured JSON format, allowing AI to easily understand and process the results.How to use the Modern CLI MCP Server?
Using the Modern CLI MCP Server is very simple: First, install the server via Nix, Docker, or binary files. Then, add the server configuration to the MCP configuration of your AI assistant (such as Claude Desktop). After the configuration is completed, the AI assistant can directly call various command-line tools, such as asking the AI to search for code, view files, manage Git repositories, or check the status of a Kubernetes cluster.Applicable scenarios
The Modern CLI MCP Server is particularly suitable for the following scenarios: 1. AI-assisted development: Let AI help search for code, analyze project structures, and manage Git operations. 2. DevOps automation: AI can check container images, scan for security vulnerabilities, and manage Kubernetes resources. 3. Data analysis and processing: AI can use tools such as jq and yq to process JSON, YAML, and XML data. 4. System monitoring and debugging: AI can view processes, analyze logs, and check system status. 5. Learning and exploration: AI can help you learn how to use various command-line tools.Main features
Rich toolset
Provides 107 carefully selected modern command-line tools, covering multiple areas such as file system, search, Git, containers, Kubernetes, data processing, network, and system monitoring.
AI-optimized output
The outputs of all tools are automatically converted into structured JSON format for easy parsing and understanding by AI. This avoids the problem of unstructured output of traditional command lines.
Zero-configuration dependency management
Through the Nix package manager, all tool dependencies are automatically resolved, eliminating the need to manually install 107 tools. The Docker version also includes all necessary tools.
Intelligent access control
Supports the.agentignore file to control which files the AI can access, protecting sensitive data and configuration files from accidental access by AI.
Multi-platform support
Supports three running modes: Nix, Docker, and native binary, and can be deployed and used in various environments.
Git integration
Deeply integrates with GitHub and GitLab APIs, allowing AI to directly operate on repositories, issues, pull requests, workflows, etc.
Advantages
One-stop solution: No need to configure AI access for each tool separately
Structured output: All tool outputs are in JSON format, enabling more accurate AI processing
Secure and controllable: Fine-grained control of AI's file access permissions through.agentignore
Easy to deploy: Multiple installation methods to meet different environmental requirements
Continuous update: The toolset will be continuously updated and improved with the development of the community
Performance optimization: Uses modern and efficient command-line tools (such as fd and rg instead of traditional find and grep)
Limitations
Learning curve: Requires an understanding of the basic concepts and configuration methods of the MCP protocol
Resource consumption: Contains 107 tools, resulting in a large Docker image and Nix package size
Permission restrictions: AI can only access files and directories that are explicitly allowed
Network dependency: Some Git and container operations require a network connection
Platform limitations: Some tools may vary on different operating systems
How to use
Choose an installation method
Choose a suitable installation method according to your environment:
- Nix (recommended): The simplest, automatically manages all dependencies
- Docker: Suitable for containerized environments
- Binary files: Run the pre-compiled version directly
Configure the AI assistant
Add the server configuration to the MCP configuration file of your AI assistant (such as Claude Desktop). The configuration file is usually located at:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%\Claude\claude_desktop_config.json
- Linux: ~/.config/Claude/claude_desktop_config.json
Configure access control (optional)
Create an.agentignore file in the project root directory to specify the files and directories that the AI cannot access. This can protect sensitive information such as API keys and configuration files.
Start using
Restart the AI assistant. Now you can ask the AI to use various command-line tools. For example, you can say:
- "View the files in the current directory"
- "Search for code containing TODO"
- "Check the status of Docker containers"
- "View information about a GitHub repository"
Usage examples
Code review assistant
AI can assist in code review, search for potential issues, and check code quality.
Project analysis report
AI can analyze project structures, provide statistical information, and insights.
DevOps monitoring
AI can monitor the status of containers and Kubernetes environments.
Data processing and conversion
AI can help process data files in various formats.
Git operation automation
AI can assist in daily Git and GitHub operations.
Frequently Asked Questions
Do I need to install all 107 command-line tools?
Can AI access all files on my computer?
Which AI assistants does this server support?
How to add new command-line tools?
Is the output always in JSON format?
Will this server affect my system performance?
How to update to the latest version?
Does it support the Windows system?
Related resources
Official documentation
Complete official documentation, including a detailed tool list, API reference, and configuration guide
GitHub repository
Source code, issue tracking, and contribution guide
Docker image
Official Docker image repository
FlakeHub page
Nix Flake version management and release
Model Context Protocol
Official specification of the MCP protocol
Claude MCP documentation
Official MCP development documentation from Anthropic
Detailed tool list
Detailed descriptions and usage examples of all 107 tools

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