MCP Tracing
Phoenix provides tracing for MCP clients and servers through OpenInference. This includes the unique capability to trace client to server interactions under a single trace in the correct hierarchy.
The openinference-instrumentation-mcp instrumentor is unique compared to other OpenInference instrumentors. It does not generate any of its own telemetry. Instead, it enables context propagation between MCP clients and servers to unify traces. You still need generate OpenTelemetry traces in both the client and server to see a unified trace.
Install
pip install openinference-instrumentation-mcpBecause the MCP instrumentor does not generate its own telemetry, you must use it alongside other instrumentation code to see traces.
The example code below uses OpenAI agents, which you can instrument using:
pip install openinference-instrumentation-openai-agentsAdd Tracing to your MCP Client
import asyncio from agents import Agent, Runner from agents.mcp import MCPServer, MCPServerStdio from dotenv import load_dotenv from phoenix.otel import register load_dotenv() # Connect to your Phoenix instance tracer_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())Add Tracing to your MCP Server
import json import os from datetime import datetime, timedelta import openai from dotenv import load_dotenv from mcp.server.fastmcp import FastMCP from pydantic import BaseModel from phoenix.otel import register load_dotenv() # You must also connect your MCP server to Phoenix tracer_provider = register(auto_instrument=True) # Get a tracer to add additional instrumentattion tracer = tracer_provider.get_tracer("financial-analysis-server") # Configure OpenAI client client = openai.OpenAI(api_key=os.environ.get("OPENAI_API_KEY")) MODEL = "gpt-4-turbo" # Create MCP server mcp = 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 method def 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 wish if __name__ == "__main__": mcp.run()Observe
Now that you have tracing setup, all invocations of your client and server will be streamed to Phoenix for observability and evaluation, and connected in the platform.
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