Pokémon MCP Server

Pokémon MCP Server

Enables AI models to access comprehensive Pokémon data from PokéAPI and simulate battles between any two Pokémon with realistic mechanics including type effectiveness, stat-based damage calculations, and status effects.

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访问服务器

README

Pokémon MCP Server — Data Resource + Battle Simulation Tool

Overview

This project implements an MCP (Model Context Protocol) server that provides AI models with access to two key capabilities:

1- Pokémon Data Resource – a resource that exposes comprehensive Pokémon data from the public PokéAPI ( https://pokeapi.co/ ).

2- Battle Simulation Tool – a tool that simulates battles between any two Pokémon, including type effectiveness, stats-based damage, turn order, and basic status effects.

This server acts as a bridge between AI and the Pokémon world, enabling LLMs to both retrieve knowledge and interactively simulate battles.

Part 1: Pokémon Data Resource

Implementation

  • Connects to the public PokéAPI ( https://pokeapi.co/ )

  • Exposes comprehensive Pokémon information including:

  • Base stats: HP, Attack, Defense, Special Attack, Special Defense, Speed

  • Types (e.g., Fire, Water, Grass)

  • Abilities

  • Available moves and their effects (power, accuracy, type, effect text)

  • Evolution information

MCP Resource

  • Resource: pokemon://{name}
  • Returns JSON including stats, types, abilities, moves (with effects), and evolution chain.
  • Implements MCP resource design patterns to make this data accessible to LLMs.

Deliverables

  • Code for the MCP server with the Pokémon data resource.
  • Documentation (this README) describing how the resource exposes data.
  • Example queries (examples/llm_examples.md).

Part 2: Battle Simulation Tool

Implementation

  • Tool: simulate_battle(pokemon_a, pokemon_b, max_turns=100, seed=None)

  • Simulates a battle between any two Pokémon using:

  • Type effectiveness calculations (e.g., Water > Fire)

  • Damage calculations based on stats and move power

  • Turn order based on Speed stat

Status effects:

  • Paralysis – chance to skip a turn
  • Burn – recurring HP loss
  • Poison – recurring HP loss
  • Detailed battle logs showing each turn’s actions and outcomes
  • Winner determination (first Pokémon to faint, or higher HP after max turns)

MCP Tool

  • Exposed via MCP as a callable tool: simulate_battle
  • Returns JSON object, e.g.:
{
  "winner": "blastoise",
  "log": [
    "--- Turn 1 ---",
    "charizard used fire-punch → blastoise lost 14 HP!",
    "blastoise is now affected by burn!",
    ...
  ]
}

Deliverables

  • Code for the battle simulation tool following MCP tool specification
  • Example usage in examples/llm_examples.md

Project Packaging

The submission includes a ZIP file containing:

  • All code (pkmon_core/server.py, pkmon_core/battle.py)
  • Supporting files (requirements.txt, README.md, examples/)
  • A test script (test_part1.py)
  • Clear instructions for setup and usage

Installation & Setup

Requirements

  • Python 3.10+
  • Virtual environment recommended

Setup


git clone https://github.com/TaifAlsadoon/pokemon-mcp-server.git
cd pokemon-mcp-server

python -m venv .venv
source .venv/bin/activate  

pip install -r requirements.txt

Running & Testing

Run the MCP Server

python -m pkmon_core.server

Note: The server runs in stdio mode and will appear idle, waiting for an MCP client. Stop with Ctrl+C.

Quick Python Tests

python - << 'PY'
from pkmon_core.server import get_pokemon, simulate_battle

print("Resource sample (pikachu) ->")
print(get_pokemon("pikachu")[:200], "...\n")

print("Battle sample ->")
res = simulate_battle("charizard", "blastoise", seed=42, max_turns=20)
print("Winner:", res["winner"])
print("\n".join(res["log"][:10]))
PY

Expected output:

  • JSON data for Pikachu (types, stats, moves, evolution chain)
  • Battle log with turn-by-turn actions and a winner (e.g., Blastoise)

Examples for LLM Usage

See examples/llm_examples.md for prompt examples, such as:

  • Summarizing a Pokémon’s stats, moves, and evolution
  • Simulating a battle and explaining why the winner won

See examples/llm_examples.md for prompt examples.

Notes

  • Simplified mechanics: ignores PP, items, weather, etc.
  • Focused on clarity and educational battle simulation
  • Easily extensible to add more mechanics

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