Learn how tmux-mcp-tools simplifies managing tmux sessions with capture, send command, and write file features
tmux-mcp-tools
MCP Server?The tmux-mcp-tools
MCP Server provides a robust framework for integrating Model Context Protocol (MCP) into development environments, specifically through its enhanced capabilities in managing tmux sessions. It offers a suite of tools designed to facilitate seamless communication between AI applications and underlying data sources or tools. By leveraging the standardized protocol offered by MCP, tmux-mcp-tools
ensures that various AI applications can easily connect to the infrastructure needed to perform their functions, making development processes more efficient and versatile.
The tmux-mcp-tools
server is equipped with several key features that enhance its utility for developers. It supports core MCP commands such as tmux_capture_pane
, which enables capturing content from a tmux pane; tmux_send_command
, which sends commands to a specific tmux pane and automatically includes an Enter key hit for execution; and tmux_write_file
, designed to write content to files within tmux panes using the heredoc pattern. These features are implemented with deep integration into the MCP protocol, enabling seamless interaction between AI applications and server-side operations.
graph TD
A[AI Application] -->|MCP Client| B[MCP Server]
B --> C[Tmux Pane]
C --> D[]{"tmux_capture_pane", "tmux_send_command", "tmux_write_file"}
D --> E[Data Source/Tool]
style A fill:#e1f5fe
style B fill:#f3e5f5
style C fill:#e8f5e8
This diagram illustrates the flow of MCP commands from an AI application (using a compatible client) to the tmux-mcp-tools
server, where they are executed within tmux panes and subsequently interact with external data sources or tools.
The architecture of tmux-mcp-tools
is built on top of existing technology stacks but heavily integrates with the Model Context Protocol. It uses Node.js and Tmux to manage tmux sessions, ensuring that commands can be executed in a controlled environment. The server is designed to be extensible, allowing for future integration with other tools and data sources that might conform to MCP standards.
MCP Client | Resources | Tools | Prompts |
---|---|---|---|
Claude Desktop | ✅ | ✅ | ✅ |
Continue | ✅ | ✅ | ✅ |
Cursor | ❌ | ✅ | ❌ |
This compatibility matrix indicates that tmux-mcp-tools
is fully compatible with MCP clients such as Claude Desktop and Continue, offering support for resource management, tool usage, and prompt handling. However, Cursor currently only supports tools but lacks full MCP client compatibility.
To install the tmux-mcp-tools
server, follow these steps:
Ensure you have npm installed on your system.
Run the following command to install the package:
npx -y @modelcontextprotocol/server-tmux-mcp-tools
Optionally, configure environment variables if needed.
Start the server by running:
uvx tmux-mcp-tools
Here is a sample configuration snippet to be included in your MCP setup:
{
"mcpServers": {
"tmux-mcp-tools": {
"command": "uvx",
"args": ["tmux-mcp-tools"]
}
}
}
AI applications can use tmux-mcp-tools
to generate prompts within a tmux pane, execute them against external tools, and capture the output for further processing. This allows for dynamic workflows where complex tasks are broken down into manageable steps.
By capturing content from multiple tmux panes and writing it to file, developers can collect data generated by various processes and analyze it within a secure environment managed through MCP.
The tmux-mcp-tools
server is designed to be fully compliant with the Model Context Protocol, supporting AI applications like Claude Desktop, Continue, and Cursor. Developers looking to integrate these clients can rely on its robust command capabilities to manage tmux sessions effectively.
tmux-mcp-tools
focuses on high performance and compatibility across different environments. It has been tested with multiple AI applications under varying conditions to ensure smooth operation and reliable data handling.
For advanced users, the server supports custom configuration through environment variables and JSON configurations. These allow fine-tuning of behavior based on specific needs while ensuring security measures are in place against unauthorized access or misuse.
Q: Can tmux-mcp-tools
be used with AI applications other than those listed?
Q: Are there any known limitations or performance bottlenecks with the server when running multiple concurrent commands?
Q: How does tmux-mcp-tools
ensure security during command execution and data transfer between clients and servers?
Q: Is it possible to extend the functionality of tmux-mcp-tools
by adding new custom commands for specific tasks?
Q: Are there any known issues with legacy systems when integrating with tmux-mcp-tools
?
Contributions to the tmux-mcp-tools
project are welcome. To contribute, developers should familiarize themselves with the codebase and follow established coding standards. Detailed contribution guidelines can be found in the repository's README file.
For more information on Model Context Protocol (MCP) and compatible clients, visit the official Model Context Protocol documentation.
This comprehensive documentation aims to provide a clear understanding of tmux-mcp-tools
as an essential tool for developers building AI applications and integrations.
Learn how to use MCProto Ruby gem to create and chain MCP servers for custom solutions
AI Vision MCP Server offers AI-powered visual analysis, screenshots, and report generation for MCP-compatible AI assistants
Analyze search intent with MCP API for SEO insights and keyword categorization
MCP server for accessing and managing IMDB data with notes, summaries, and tools
Expose Chicago Public Schools data with a local MCP server accessing SQLite and LanceDB databases
Connects n8n workflows to MCP servers for AI tool integration and data access