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In professional Pokémon competition, winning is not only dependent on stat distribution of Pokémon in/ev but also the move pool they have access to. Strategies are built up using moves that setup Pokémon in play or Pokémon that will be switched to in such a manner that the stat distributions and or move priority is modified in the users favor. I wonder if there’s a way to calculate ideal team not just from stat distributions but the likelihood the available move pool can be linked to a coherent win strategy like stall, trick room switchup, flip turn into swift swim priority and other matchup strategies


https://blog.modelcontextprotocol.io/posts/2025-11-21-mcp-ap...

It’s going to be built into MCP and will be supported by Anthropic and OpenAI or anyone else that supports this mcp spec


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