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Lets an LLM care for a virtual pet with real-time state viewing.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "tamagotchi-mcp" yet — see the docs or source repo.
Connect to tamagotchi-mcp, inspect the pet's current state, and automatically feed, play with, or let it sleep based on hunger, energy, and mood, then report what you did.
The AI reads the pet's state, performs appropriate care actions, and returns a brief action summary.
Use tamagotchi-mcp to fetch the pet's latest state and summarize its health, hunger, mood, and sleep in a concise table for comparison with the web UI.
It outputs a clear status summary so the user can quickly understand the pet's current condition.
Use tamagotchi-mcp as a test environment, read the pet state 5 times in sequence, and explain why you choose feeding, playing, or resting each time to evaluate decision consistency.
You get a test result with multi-step state assessments, action choices, and explanations for each decision.
Track dog care, diet, symptoms, and get safety and risk advice.
An MCP tool for querying Petstore pets by status via LLM function calling.
Chat with an AI agent, get weather data, and monitor agent status via MCP.
Expose a pet store API as AI-callable tools for HTTP testing and integration.
Check Xiaohei's mood, hunger level, and recent play status.
Collect web data, analyze text, and generate reports or presentation content.