MCP Tracing

import asynciofrom agents import Agent, Runnerfrom agents.mcp import MCPServer, MCPServerStdiofrom dotenv import load_dotenvfrom phoenix.otel import registerload_dotenv()# Connect to your Phoenix instancetracer_provider = register(auto_instrument=True)async def run(mcp_server: MCPServer):    agent = Agent(        name="Assistant",        instructions="Use the tools to answer the users question.",        mcp_servers=[mcp_server],    )    while True:        message = input("\n\nEnter your question (or 'exit' to quit): ")        if message.lower() == "exit" or message.lower() == "q":            break        print(f"\n\nRunning: {message}")        result = await Runner.run(starting_agent=agent, input=message)        print(result.final_output)async def main():    async with MCPServerStdio(        name="Financial Analysis Server",        params={            "command": "fastmcp",            "args": ["run", "./server.py"],        },        client_session_timeout_seconds=30,    ) as server:        await run(server)if __name__ == "__main__":    asyncio.run(main())

import jsonimport osfrom datetime import datetime, timedeltaimport openaifrom dotenv import load_dotenvfrom mcp.server.fastmcp import FastMCPfrom pydantic import BaseModelfrom phoenix.otel import registerload_dotenv()# You must also connect your MCP server to Phoenixtracer_provider = register(auto_instrument=True)# Get a tracer to add additional instrumentattiontracer = tracer_provider.get_tracer("financial-analysis-server")# Configure OpenAI clientclient = openai.OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))MODEL = "gpt-4-turbo"# Create MCP servermcp = FastMCP("Financial Analysis Server")class StockAnalysisRequest(BaseModel):    ticker: str    time_period: str = "short-term"  # short-term, medium-term, long-term@mcp.tool()@tracer.tool(name="MCP.analyze_stock") # this OpenInference call adds tracing to this methoddef analyze_stock(request: StockAnalysisRequest) -> dict:    """Analyzes a stock based on its ticker symbol and provides investment recommendations."""    # Make LLM API call to analyze the stock    prompt = f"""    Provide a detailed financial analysis for the stock ticker: {request.ticker}    Time horizon: {request.time_period}    Please include:    1. Company overview    2. Recent financial performance    3. Key metrics (P/E ratio, market cap, etc.)    4. Risk assessment    5. Investment recommendation    Format your response as a JSON object with the following structure:    {{        "ticker": "{request.ticker}",        "company_name": "Full company name",        "overview": "Brief company description",        "financial_performance": "Analysis of recent performance",        "key_metrics": {{            "market_cap": "Value in billions",            "pe_ratio": "Current P/E ratio",            "dividend_yield": "Current yield percentage",            "52_week_high": "Value",            "52_week_low": "Value"        }},        "risk_assessment": "Analysis of risks",        "recommendation": "Buy/Hold/Sell recommendation with explanation",        "time_horizon": "{request.time_period}"    }}    """    response = client.chat.completions.create(        model=MODEL,        messages=[{"role": "user", "content": prompt}],        response_format={"type": "json_object"},    )    analysis = json.loads(response.choices[0].message.content)    return analysis# ... define any additional MCP tools you wishif __name__ == "__main__":    mcp.run()