iflow-mcp_theailanguage-terminal_server 0.1.0

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MCP SSE Server and STDIO Server Examples

Introduction

Welcome to The AI Language project! In this repository, you'll find multiple examples of setting up MCP Servers. MCP (Model Context Protocol) is a framework for AI models that enables them to store data, run tools, and use prompts for specific tasks.

Available Server Examples

We provide four examples to help you set up your MCP server in different environments. The table below summarizes each configuration:

Example	Server Type	Transport Method	Environment	Docker	Tutorial Link
1	Terminal Server (STDIO)	STDIO	Local	No	Tutorial 1
2	Terminal Server (STDIO)	STDIO	Local	Yes	Tutorial 2
3	Terminal Server (SSE)	SSE	Local	Yes	Tutorial 3
4	Terminal Server (SSE)	SSE	Google Cloud Platform (Web)	Yes	Tutorial 3

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What is MCP?

MCP (Model Context Protocol) is a protocol that allows AI models to:

• Store data (like files or API responses)
• Run tools (functions that AI can execute)
• Use prompts (predefined templates for tasks)

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Option 1: Setup Without Docker (Local Python)

This option demonstrates how to set up an MCP server locally using Python without Docker. Follow the video tutorial: Tutorial 1

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Option 2: Setup With Docker

This option shows how to containerize the MCP server with Docker and run it locally. Follow the video tutorial: Tutorial 2

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Option 3: Setup with SSE (Local, Docker)

This option demonstrates how to run an MCP server over SSE using Docker in a local environment. Follow the video tutorial: Tutorial 3

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Option 4: Setup with SSE on Google Cloud Platform

This option details how to deploy the SSE server to Google Cloud Platform using Docker. Follow the video tutorial: Tutorial 3

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Testing the MCP Server

Once the server is running, you can test it by using prompts in Claude, such as:

• Run the command ls in my workspace.
• Execute echo Hello from Claude.

You should see the output directly from your terminal server 🎉

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Wrapping Up

Congrats! You've successfully built an MCP server that can execute terminal commands. You can run it locally or in Docker, depending on your preference.

Next Steps:

• Add security checks to block potentially dangerous commands.
• Allow Claude to read and write files.
• Connect the server to cloud systems or remote environments.

For any issues or improvements, feel free to contribute and open an issue or pull request in this repository!

🤝 Contributing

At this time, this project does not accept external code contributions.

This is to keep licensing simple and avoid any shared copyright.

You're very welcome to: ✅ Report bugs or request features (via GitHub Issues)
✅ Fork the repo and build your own version
✅ Suggest documentation improvements

If you'd like to collaborate in another way, feel free to open a discussion!