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Pokemon-mcp

by Zenith-Mind
mcp_client_openai.py2.23 kB
from langchain_openai import ChatOpenAI from contextlib import asynccontextmanager from langchain_mcp_adapters.client import MultiServerMCPClient from langgraph.prebuilt import create_react_agent import asyncio import os if os.getenv('OPENAI_API_KEY'): llm = ChatOpenAI(model="o3-mini") else: print('Export OPENAI_API_KEY to initialize OpenAI LLM.') exit(1) @asynccontextmanager async def main(): client = MultiServerMCPClient({ "pokemon_server": { "url": os.getenv("POKEMON_MCP_URL", "http://localhost:8000/sse"), "transport": "sse" } }) tools = await client.get_tools() # Filter tools to include only Pokémon-related tools pokemon_tools = [tool for tool in tools if tool.name in [ "get_pokemon", "compare_pokemon", "get_type_matchups", "suggest_team" ]] print("Loaded Pokémon MCP tools: " + ", ".join(tool.name for tool in pokemon_tools)) agent = create_react_agent( llm, tools=pokemon_tools, prompt="""You are a Pokémon expert assistant. You have access to tools that can: - Get detailed information about any Pokémon - Compare two Pokémon's attributes - Analyze type matchups and strategies - Suggest balanced team compositions Use these tools to provide comprehensive and helpful responses about Pokémon.""" ) yield agent async def invoke_agent(query): async with main() as agent: agent_response = await agent.ainvoke({"messages": query}) print("==== Final Answer ====") print(agent_response['messages'][-1].content) if __name__ == "__main__": # Example queries for different functionalities queries = [ "Tell me about Charizard and its strengths", "Compare Pikachu and Raichu", "What are the best type matchups against Gyarados?", "Suggest a balanced team with a strong fire attacker and good defense" ] # You can change this to test different queries selected_query = queries[0] # Change index to test different queries print(f"Query: {selected_query}") asyncio.run(invoke_agent(query=selected_query))

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