mcp_client_debug.py

import traceback
import logging
import sys
import os
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_ollama.chat_models import ChatOllama
from langchain_deepseek import ChatDeepSeek
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse
from langchain_core.messages import AIMessage

from pydantic import BaseModel
import uvicorn
if not os.getenv("DEEPSEEK_API_KEY"):
    os.environ["DEEPSEEK_API_KEY"] = ""



# Configure logging
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[logging.StreamHandler(sys.stdout)]
)
logger = logging.getLogger("mcp_client")

# Define server configuration once
MCP_SERVER_CONFIG = {
    "gatherings": {
      "command": "python",
      "args": ["gatherings_mcp_server_single.py"],
      "transport": "stdio"
    }
}

# Debug mode - set to True to include detailed error info in responses
DEBUG_MODE = True

app = FastAPI(title="MCP Client API (Debug Mode)")

@app.exception_handler(Exception)
async def global_exception_handler(request: Request, exc: Exception):
    """Global exception handler to log all exceptions"""
    error_detail = {
        "error": str(exc),
        "traceback": traceback.format_exc() if DEBUG_MODE else "Set DEBUG_MODE=True for traceback"
    }
    logger.error(f"Unhandled exception: {str(exc)}\n{traceback.format_exc()}")
    return JSONResponse(status_code=500, content=error_detail)

@app.get("/health")
async def health_check():
    return {"status": "ok"}

@app.get("/tools")
async def list_tools():
    """Endpoint to explicitly list all available tools without invoking the agent"""
    try:
        logger.info("Listing available tools")
        
        logger.debug("Initializing MultiServerMCPClient")
        async with MultiServerMCPClient(MCP_SERVER_CONFIG) as client:
            try:
                logger.debug("Getting tools from MCP Client")
                tools = client.get_tools()
                
                # Format tools in a more readable way
                tool_info = []
                for tool in tools:
                    tool_data = {
                        "name": tool.name,
                        "description": tool.description,
                        "parameters": str(tool.args)
                    }
                    tool_info.append(tool_data)
                
                logger.debug(f"Retrieved {len(tools)} tools")
                return {
                    "status": "success",
                    "count": len(tools),
                    "tools": tool_info,
                    "server_config": MCP_SERVER_CONFIG
                }
            except Exception as e:
                logger.error(f"Error listing tools: {str(e)}")
                logger.error(traceback.format_exc())
                error_detail = {
                    "error": str(e),
                    "traceback": traceback.format_exc() if DEBUG_MODE else None
                }
                raise HTTPException(status_code=500, detail=error_detail)
    except Exception as e:
        logger.error(f"Unhandled exception in list_tools: {str(e)}")
        logger.error(traceback.format_exc())
        error_detail = {
            "error": str(e),
            "traceback": traceback.format_exc() if DEBUG_MODE else None
        }
        raise HTTPException(status_code=500, detail=error_detail)

class MessageRequest(BaseModel):
    message: str

@app.post("/message")
async def process_message(request: MessageRequest):
    try:
        logger.info(f"Processing message: {request.message}")
        
        # Initialize model
        logger.debug("Initializing ChatOllama model")
        model = ChatOllama(model="llama3.1", temperature=0.3)
        # logger.debug("Initializing ChatDeepSeek model")
        # model = ChatDeepSeek(model="deepseek-chat", temperature=0)
        logger.debug("Initializing MultiServerMCPClient")
        async with MultiServerMCPClient(MCP_SERVER_CONFIG) as client:
            try:
                logger.debug("Getting tools from MCP Client")
                tools = client.get_tools()
                logger.debug(f"Retrieved {len(tools)} tools")
                
                # Log tool names for debugging
                tool_names = [tool.name for tool in tools]
                logger.debug(f"Available tools: {tool_names}")
                
                # Check if the message is specifically asking for tools
                if any(keyword in request.message.lower() for keyword in ["what tools", "list tools", "available tools"]):
                    tool_descriptions = [f"- {tool.name}: {tool.description}" for tool in tools]
                    response_content = "I have the following tools available:\n" + "\n".join(tool_descriptions)
                    
                    # Format response according to documented structure
                    return {
                        "response": {
                            "messages": [
                                {
                                    "content": response_content,
                                    "type": "ai",
                                    "tool_calls": [],
                                    "tool_call_id": None
                                }
                            ],
                            "return_values": {
                                "output": response_content
                            }
                        },
                        "status": "success"
                    }
                
                logger.debug("Creating react agent")
                agent = create_react_agent(model, tools)
                
                logger.debug("Invoking agent with message")
                # Format input as messages which LangGraph ReAct agent expects
                agent_response = await agent.ainvoke({
                    "messages": [{"role": "user", "content": request.message}]
                })
                logger.info("Agent response received successfully")
                logger.debug(f"Agent response type: {type(agent_response)}")
                logger.debug(f"Agent response details: {agent_response}")
                
                # Improved response handling logic
                if isinstance(agent_response, AIMessage):
                    response_content = agent_response.content
                elif isinstance(agent_response, dict) and "messages" in agent_response:
                    # Handle dict with messages
                    messages = agent_response["messages"]
                    if messages and len(messages) > 0:
                        last_message = messages[-1]
                        if isinstance(last_message, dict) and "content" in last_message:
                            response_content = last_message["content"]
                        elif hasattr(last_message, "content"):
                            response_content = last_message.content
                        else:
                            response_content = str(last_message)
                    else:
                        response_content = str(agent_response)
                elif hasattr(agent_response, "messages"):
                    # Handle object with messages attribute
                    messages = agent_response.messages
                    if messages and len(messages) > 0:
                        last_message = messages[-1]
                        if hasattr(last_message, "content"):
                            response_content = last_message.content
                        else:
                            response_content = str(last_message)
                    else:
                        response_content = str(agent_response)
                else:
                    # Last resort fallback
                    response_content = str(agent_response)
                
                logger.debug(f"Extracted response content: {response_content}")
                
                # Format response according to documented structure
                return {
                    "response": {
                        "messages": [
                            {
                                "content": response_content,
                                "type": "ai",
                                "tool_calls": [],
                                "tool_call_id": None
                            }
                        ],
                        "return_values": {
                            "output": response_content
                        }
                    },
                    "status": "success"
                }
            except Exception as e:
                logger.error(f"Error during agent processing: {str(e)}")
                logger.error(traceback.format_exc())
                error_detail = {
                    "error": str(e),
                    "traceback": traceback.format_exc() if DEBUG_MODE else None
                }
                raise HTTPException(status_code=500, detail=error_detail)
    except Exception as e:
        logger.error(f"Unhandled exception in process_message: {str(e)}")
        logger.error(traceback.format_exc())
        error_detail = {
            "error": str(e),
            "traceback": traceback.format_exc() if DEBUG_MODE else None
        }
        raise HTTPException(status_code=500, detail=error_detail)

if __name__ == "__main__":
    # Start the FastAPI server when the script is run directly
    logger.info("Starting FastAPI server in debug mode")
    uvicorn.run(app, host="0.0.0.0", port=8000)