MCP Terminal Server
by The AI Language
This repository provides examples of setting up MCP (Model Context Protocol) servers, enabling AI models to interact with the environment. It includes configurations for local and cloud deployments using STDIO and SSE transport methods.
Last updated: N/A
What is MCP Terminal Server?
This project provides examples of MCP servers, which allow AI models to store data, run tools, and use prompts for specific tasks. The examples focus on terminal servers that can execute commands.
How to use MCP Terminal Server?
The repository provides four examples with video tutorials. You can choose to set up the server locally with or without Docker, or deploy it to Google Cloud Platform. Follow the corresponding video tutorial for detailed instructions.
Key features of MCP Terminal Server
Enables AI models to execute terminal commands
Supports STDIO and SSE transport methods
Provides Dockerized deployment options
Includes examples for local and cloud environments
Use cases of MCP Terminal Server
Allowing AI models to interact with the operating system
Automating tasks through AI-driven command execution
Integrating AI models with cloud services
Building AI agents that can perform system administration tasks
FAQ from MCP Terminal Server
What is MCP?
What is MCP?
MCP (Model Context Protocol) is a protocol that allows AI models to store data, run tools, and use prompts.
What transport methods are supported?
What transport methods are supported?
The examples use STDIO and SSE (Server-Sent Events) for communication.
Can I deploy this to the cloud?
Can I deploy this to the cloud?
Yes, there is an example for deploying the SSE server to Google Cloud Platform.
Do I need Docker?
Do I need Docker?
No, there is an example for setting up the server locally without Docker, but Docker is used in other examples.
Can I contribute code to this project?
Can I contribute code to this project?
At this time, the project does not accept external code contributions, but you can report bugs, request features, fork the repo, or suggest documentation improvements.