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Tamagotchi MCP Server

by wai-imyen

This is a simple Tamagotchi MCP Server implementation that simulates a virtual pet game. It allows users to interact with a virtual pet through various actions.

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What is Tamagotchi MCP Server?

The Tamagotchi MCP Server is a Python-based server that simulates the classic Tamagotchi virtual pet game. It allows users to interact with a virtual pet by performing actions such as feeding, playing, and cleaning.

How to use Tamagotchi MCP Server?

To use the server, first set up the environment by installing Python 3.11 and uvicorn. Then, create a virtual environment, activate it, and install the dependencies from the requirements.txt file. Start the FastAPI server using the provided uvicorn command. Finally, configure Claude Desktop with the provided JSON configuration, adjusting the paths to match your local setup. Interact with the server by sending commands to the configured endpoint.

Key features of Tamagotchi MCP Server

  • Simulates a Tamagotchi virtual pet

  • Implemented using Python and FastAPI

  • Provides basic actions like feeding, playing, and cleaning

  • Configurable for use with Claude Desktop

Use cases of Tamagotchi MCP Server

  • Creating a virtual pet game experience

  • Integrating a virtual pet into a larger application

  • Learning about server-side development with Python and FastAPI

  • Experimenting with MCP server implementations

FAQ from Tamagotchi MCP Server

What is MCP?

MCP stands for Multi-Client Protocol. It's a protocol used for communication between multiple clients and a server.

What is FastAPI?

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.

What is uvicorn?

Uvicorn is an ASGI (Asynchronous Server Gateway Interface) web server implementation for Python.

How do I customize the Tamagotchi's behavior?

The behavior is currently hardcoded in the server.py file. You can modify the code to change how the Tamagotchi responds to different actions.

Where can I find more information about the implementation?