Initial commit

mcp-server/main.py

@@ -0,0 +1,186 @@
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import Dict, Any, List
+import logging
+
+from config import settings
+from claude_client import claude_client
+from perplexity_client import perplexity_client
+from mcp_tools import mcp_tools
+
+# AI 클라이언트 선택
+ai_client = perplexity_client if settings.AI_PROVIDER == "perplexity" else claude_client
+
+# 로깅 설정
+logging.basicConfig(
+    level=getattr(logging, settings.LOG_LEVEL.upper(), logging.INFO),
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+logger = logging.getLogger(__name__)
+
+app = FastAPI(
+    title="Auto Stock Trading MCP Server",
+    description="Model Context Protocol Server for Stock Trading with Claude AI",
+    version="1.0.0"
+)
+
+# CORS 설정
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class ToolCallRequest(BaseModel):
+    tool_name: str
+    parameters: Dict[str, Any]
+
+
+class AnalysisRequest(BaseModel):
+    analysis_type: str  # RULE_VALIDATION, MARKET_ANALYSIS, RISK_ASSESSMENT
+    data: Dict[str, Any]
+
+
+@app.get("/")
+async def root():
+    return {
+        "message": "MCP Server for Auto Stock Trading",
+        "version": "1.0.0",
+        "status": "running"
+    }
+
+
+@app.get("/health")
+async def health_check():
+    return {
+        "status": "healthy",
+        "ai_provider": settings.AI_PROVIDER,
+        "claude_configured": bool(settings.ANTHROPIC_API_KEY),
+        "perplexity_configured": bool(settings.PERPLEXITY_API_KEY)
+    }
+
+
+@app.get("/tools")
+async def list_tools():
+    """사용 가능한 도구 목록"""
+    return {
+        "tools": mcp_tools.get_tool_definitions()
+    }
+
+
+@app.post("/tools/call")
+async def call_tool(request: ToolCallRequest):
+    """MCP 도구 호출"""
+    try:
+        result = await mcp_tools.call_tool(request.tool_name, request.parameters)
+        return result
+    except Exception as e:
+        logger.error(f"Tool call error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/analyze")
+async def analyze(request: AnalysisRequest):
+    """Claude를 통한 분석"""
+    try:
+        analysis_type = request.analysis_type
+        data = request.data
+        
+        if analysis_type == "RULE_VALIDATION":
+            # 규칙 검증
+            rule = data.get("rule")
+            market_data = data.get("market_data")
+            
+            if not rule or not market_data:
+                raise HTTPException(
+                    status_code=400,
+                    detail="rule and market_data are required"
+                )
+            
+            result = await ai_client.validate_trading_rule(rule, market_data)
+            return {
+                "analysis_type": analysis_type,
+                "result": result
+            }
+        
+        elif analysis_type == "MARKET_ANALYSIS":
+            # 시장 분석
+            stock_code = data.get("stock_code")
+            market_data = data.get("market_data")
+            news = data.get("news", [])
+            
+            if not stock_code or not market_data:
+                raise HTTPException(
+                    status_code=400,
+                    detail="stock_code and market_data are required"
+                )
+            
+            result = await ai_client.analyze_market(stock_code, market_data, news)
+            return {
+                "analysis_type": analysis_type,
+                "result": result
+            }
+        
+        elif analysis_type == "RISK_ASSESSMENT":
+            # 리스크 평가
+            portfolio = data.get("portfolio")
+            trade_plan = data.get("trade_plan")
+            
+            if not portfolio or not trade_plan:
+                raise HTTPException(
+                    status_code=400,
+                    detail="portfolio and trade_plan are required"
+                )
+            
+            result = await ai_client.assess_risk(portfolio, trade_plan)
+            return {
+                "analysis_type": analysis_type,
+                "result": result
+            }
+        
+        elif analysis_type == "FAIR_PRICE":
+            # 적정가 분석
+            stock_code = data.get("stock_code")
+            market_data = data.get("market_data")
+            financial_data = data.get("financial_data")
+            
+            if not stock_code or not market_data:
+                raise HTTPException(
+                    status_code=400,
+                    detail="stock_code and market_data are required"
+                )
+            
+            result = await ai_client.calculate_fair_price(
+                stock_code, market_data, financial_data
+            )
+            return {
+                "analysis_type": analysis_type,
+                "result": result
+            }
+        
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Unknown analysis type: {analysis_type}"
+            )
+    
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Analysis error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(
+        "main:app",
+        host=settings.MCP_SERVER_HOST,
+        port=settings.MCP_SERVER_PORT,
+        reload=True
+    )