MCPet

Overview

MCPet turns the classic virtual‑pet experience into a modern, AI‑powered interaction that runs inside any Model Context Protocol (MCP) workflow. By exposing a small set of intuitive tools—create, check, feed, play, clean, and sleep—the server lets an assistant such as Claude manage a digital companion whose state persists across sessions. The pet’s attributes (hunger, happiness, health, energy, cleanliness) evolve automatically over time and respond to the user’s actions, so even when the assistant is offline the pet continues to age and change. This continuous lifecycle model mirrors real‑world caregiving, providing a rich, engaging context for developers to experiment with stateful AI interactions.

The server’s value lies in its demonstration of core MCP concepts while remaining fully functional. Developers can quickly adopt the pet service into their own assistants, using it as a sandbox for testing persistent data handling, timed updates, and multi‑step tool orchestration. Because the pet’s state is stored in a writable directory specified by an environment variable, the service can be run locally or hosted on any platform that supports MCP. The ASCII‑art animations add a visual dimension to the protocol’s otherwise text‑centric exchanges, offering instant feedback and making it easier to showcase tool behavior in demos or tutorials.

Key features include:

• Four distinct pet types (cat, dog, dragon, alien) with personality‑driven responses.
• Four life stages (baby, child, teen, adult) that affect stat growth and available activities.
• Five core stats that track well‑being, each with natural decay or recovery mechanics.
• A toolset that covers the full care cycle: adoption, status queries, feeding with three food tiers, multiple play games, bathing, and sleeping.
• Dynamic ASCII animations that animate actions such as eating, playing, or resting, enhancing user engagement without requiring a graphical UI.

Real‑world scenarios benefit from MCPet’s design. In educational settings, students can learn about state management and time‑based updates by building custom pet behaviors. In product demos, a virtual companion can serve as an interactive mascot that follows user preferences and logs usage patterns for analytics. For research, the pet can act as a testbed for reinforcement learning agents that learn optimal care strategies over time. The server’s simple, well‑defined API also makes it an excellent candidate for integration into larger AI workflows—such as pairing a language model with a monitoring service that triggers reminders to feed or play when stats fall below thresholds.

By blending nostalgia, persistent state, and real‑time interaction, MCPet showcases how an MCP server can provide a compelling, reusable experience. Its straightforward tool definitions and animation support give developers a ready‑made example of how to build, host, and consume stateful services that enrich AI assistants with ongoing, personalized content.