# """
# Stage 2: MCP Executor - Parallel API Execution
# """

# import asyncio
# import time
# from typing import List, Dict, Any

# from .servers.weather import WeatherServer
# from .servers.soil import SoilPropertiesServer
# from .servers.water import WaterServer
# from .servers.elevation import ElevationServer
# from .servers.pests import PestsServer


# # MCP Server Registry
# MCP_SERVER_REGISTRY = {
#     "weather": {
#         "name": "Weather Server (Open-Meteo)",
#         "description": "Current weather and 7-day forecasts: temperature, precipitation, wind, humidity",
#         "capabilities": ["current_weather", "weather_forecast", "rainfall_prediction", "temperature_trends"],
#         "use_for": ["rain", "temperature", "weather", "forecast", "frost", "wind"]
#     },
#     "soil_properties": {
#         "name": "Soil Properties Server (SoilGrids)",
#         "description": "Soil composition: clay, sand, silt, pH, organic matter from global soil database",
#         "capabilities": ["soil_texture", "soil_ph", "clay_content", "sand_content", "nutrients"],
#         "use_for": ["soil", "pH", "texture", "clay", "sand", "composition", "fertility", "nutrients"]
#     },
#     "water": {
#         "name": "Groundwater Server (GRACE)",
#         "description": "Groundwater levels and drought indicators from NASA GRACE satellite data",
#         "capabilities": ["groundwater_levels", "drought_status", "water_storage", "soil_moisture"],
#         "use_for": ["groundwater", "drought", "water", "irrigation", "water stress", "moisture"]
#     },
#     "elevation": {
#         "name": "Elevation Server (OpenElevation)",
#         "description": "Field elevation and terrain data for irrigation planning",
#         "capabilities": ["elevation", "terrain_analysis"],
#         "use_for": ["elevation", "slope", "terrain", "drainage"]
#     },
#     "pests": {
#         "name": "Pest Observation Server (iNaturalist)",
#         "description": "Recent pest and insect observations from community reporting",
#         "capabilities": ["pest_observations", "disease_reports", "pest_distribution"],
#         "use_for": ["pests", "insects", "disease", "outbreak"]
#     }
# }


# class MCPExecutor:
#     """Stage 2: Execute API calls in parallel"""

#     def __init__(self):
#         self.servers = {
#             "weather": WeatherServer(),
#             "soil_properties": SoilPropertiesServer(),
#             "water": WaterServer(),
#             "elevation": ElevationServer(),
#             "pests": PestsServer()
#         }

#     async def execute_parallel(self, server_names: List[str], lat: float, lon: float) -> Dict[str, Any]:
#         """
#         Call multiple servers simultaneously
        
#         Returns:
#             {
#                 "results": {
#                     "weather": {"status": "success", "data": {...}},
#                     ...
#                 },
#                 "execution_time_seconds": float
#             }
#         """
#         start_time = time.time()

#         tasks = []
#         valid_servers = []

#         for name in server_names:
#             if name in self.servers:
#                 tasks.append(self.servers[name].get_data(lat, lon))
#                 valid_servers.append(name)
#             else:
#                 print(f"⚠️ Unknown server: {name}")

#         # Execute all in parallel
#         results = await asyncio.gather(*tasks, return_exceptions=True)

#         # Format results
#         formatted_results = {}
#         for i, server_name in enumerate(valid_servers):
#             result = results[i]
#             if isinstance(result, Exception):
#                 formatted_results[server_name] = {
#                     "status": "error",
#                     "error": str(result)
#                 }
#             else:
#                 formatted_results[server_name] = result

#         elapsed_time = time.time() - start_time

#         return {
#             "results": formatted_results,
#             "execution_time_seconds": round(elapsed_time, 2)
#         }

# """
# MCP Executor - Stage 2
# Executes parallel calls to MCP servers based on routing decisions
# """

# from typing import Dict, Any
# from concurrent.futures import ThreadPoolExecutor, as_completed
# import asyncio


# class MCPExecutor:
#     """
#     Executes MCP server calls based on routing decisions.
#     Integrates with existing server implementations in src/servers/
#     Handles both sync and async server methods.
#     """
    
#     def __init__(self, servers: Dict[str, Any]):
#         """
#         Initialize executor with MCP server instances.
        
#         Args:
#             servers: Dict mapping server names to initialized server objects
#                     e.g., {"weather": WeatherServer(), "soil": SoilPropertiesServer(), ...}
#         """
#         self.servers = servers
    
#     def execute_parallel(self, routing: Dict[str, bool], location: Dict[str, float]) -> Dict[str, Any]:
#         """
#         Execute MCP server calls in parallel based on routing.
        
#         Args:
#             routing: Simple dict with server names as keys and True/False as values
#             location: Dict with 'latitude' and 'longitude' keys
            
#         Returns:
#             Dict mapping server names to their results with metadata
#         """
#         results = {}
#         tasks = []
        
#         # Prepare tasks for servers marked for querying
#         for server_name, should_query in routing.items():
#             if should_query and server_name in self.servers:
#                 tasks.append({
#                     "server_name": server_name,
#                     "server": self.servers[server_name],
#                     "location": location
#                 })
        
#         # Execute in parallel using ThreadPoolExecutor
#         with ThreadPoolExecutor(max_workers=5) as executor:
#             futures = {
#                 executor.submit(self._call_server_sync, task): task 
#                 for task in tasks
#             }
            
#             for future in as_completed(futures):
#                 task = futures[future]
#                 server_name = task["server_name"]
                
#                 try:
#                     result = future.result(timeout=30)
#                     results[server_name] = {
#                         "data": result,
#                         "status": "success"
#                     }
#                     print(f"✓ {server_name.upper()}: Retrieved successfully")
                    
#                 except Exception as e:
#                     results[server_name] = {
#                         "data": None,
#                         "status": "error",
#                         "error": str(e)
#                     }
#                     print(f"✗ {server_name.upper()}: Error - {str(e)}")
        
#         return results
    
#     def _call_server_sync(self, task: Dict[str, Any]) -> Any:
#         """
#         Call individual MCP server, handling both sync and async methods.
        
#         Args:
#             task: Dict containing server, location, and metadata
            
#         Returns:
#             Server response data
#         """
#         server = task["server"]
#         location = task["location"]
        
#         # Try async method first (most of your servers use async)
#         if hasattr(server, 'get_data'):
#             method = getattr(server, 'get_data')
            
#             # Check if it's async
#             if asyncio.iscoroutinefunction(method):
#                 # Run async method in new event loop
#                 try:
#                     loop = asyncio.new_event_loop()
#                     asyncio.set_event_loop(loop)
#                     result = loop.run_until_complete(
#                         method(location['latitude'], location['longitude'])
#                     )
#                     loop.close()
#                     return result
#                 except Exception as e:
#                     raise Exception(f"Async execution failed: {str(e)}")
#             else:
#                 # Sync method
#                 return method(location['latitude'], location['longitude'])
        
#         # Fallback to other method names
#         elif hasattr(server, 'query'):
#             return server.query(location)
#         elif hasattr(server, 'fetch_data'):
#             return server.fetch_data(location['latitude'], location['longitude'])
#         else:
#             raise AttributeError(f"Server {task['server_name']} has no compatible query method")
    
#     def execute_sequential(self, routing: Dict[str, bool], location: Dict[str, float]) -> Dict[str, Any]:
#         """
#         Execute MCP server calls sequentially (fallback if parallel fails).
        
#         Args:
#             routing: Simple dict with server names as keys and True/False as values
#             location: Dict with 'latitude' and 'longitude' keys
            
#         Returns:
#             Dict mapping server names to their results
#         """
#         results = {}
        
#         for server_name, should_query in routing.items():
#             if should_query and server_name in self.servers:
#                 try:
#                     task = {
#                         "server_name": server_name,
#                         "server": self.servers[server_name],
#                         "location": location
#                     }
                    
#                     result = self._call_server_sync(task)
#                     results[server_name] = {
#                         "data": result,
#                         "status": "success"
#                     }
#                     print(f"✓ {server_name.upper()}: Retrieved successfully")
                    
#                 except Exception as e:
#                     results[server_name] = {
#                         "data": None,
#                         "status": "error",
#                         "error": str(e)
#                     }
#                     print(f"✗ {server_name.upper()}: Error - {str(e)}")
        
#         return results
        
#         return results
"""
MCP Executor - Stage 2
Executes parallel calls to MCP servers based on routing decisions
FIXED: 
1. Proper async handling for FastAPI (no asyncio.run inside existing loop)
2. Fixed double-wrapping of server results
"""

from typing import Dict, Any
import asyncio
import inspect


class MCPExecutor:
    """
    Executes MCP server calls based on routing decisions.
    Properly handles async servers within FastAPI's event loop.
    """
    
    def __init__(self, servers: Dict[str, Any]):
        """
        Initialize executor with MCP server instances.
        
        Args:
            servers: Dict mapping server names to initialized server objects
        """
        self.servers = servers
    
    async def execute_parallel_async(self, routing: Dict[str, bool], location: Dict[str, float]) -> Dict[str, Any]:
        """
        Execute MCP server calls in parallel (async version for FastAPI).
        
        Args:
            routing: Dict with server names as keys and True/False as values
            location: Dict with 'latitude' and 'longitude' keys
            
        Returns:
            Dict mapping server names to their results
        """
        results = {}
        tasks = []
        server_names = []
        
        for server_name, should_query in routing.items():
            if should_query and server_name in self.servers:
                server = self.servers[server_name]
                tasks.append(self._call_server(server, server_name, location))
                server_names.append(server_name)
        
        if not tasks:
            return results
        
        # Execute all tasks concurrently
        task_results = await asyncio.gather(*tasks, return_exceptions=True)
        
        # Process results
        for server_name, result in zip(server_names, task_results):
            if isinstance(result, Exception):
                results[server_name] = {
                    "data": None,
                    "status": "error",
                    "error": str(result)
                }
                print(f"✗ {server_name.upper()}: Error - {str(result)}")
            else:
                # FIX: Handle servers that return {"status": ..., "data": ...}
                # Don't double-wrap!
                if isinstance(result, dict) and "status" in result:
                    # Server already returned proper format
                    if result.get("status") == "success":
                        results[server_name] = {
                            "data": result.get("data"),  # Extract actual data
                            "status": "success"
                        }
                    else:
                        results[server_name] = {
                            "data": None,
                            "status": "error",
                            "error": result.get("error", "Unknown error")
                        }
                else:
                    # Server returned raw data
                    results[server_name] = {
                        "data": result,
                        "status": "success"
                    }
                print(f"✓ {server_name.upper()}: Retrieved successfully")
        
        return results
    
    def execute_parallel(self, routing: Dict[str, bool], location: Dict[str, float]) -> Dict[str, Any]:
        """
        Execute MCP server calls in parallel (sync wrapper).
        
        Detects if we're already in an async context and handles appropriately.
        """
        try:
            # Check if there's already a running event loop
            loop = asyncio.get_running_loop()
            # We're in an async context - need to use nest_asyncio or return a coroutine
            # For FastAPI, the endpoint should be async and call execute_parallel_async directly
            raise RuntimeError(
                "execute_parallel called from async context. "
                "Use 'await executor.execute_parallel_async()' instead."
            )
        except RuntimeError:
            # No running loop - safe to use asyncio.run
            return asyncio.run(self.execute_parallel_async(routing, location))
    
    async def _call_server(self, server: Any, server_name: str, location: Dict[str, float]) -> Any:
        """
        Call individual MCP server, handling both sync and async methods.
        """
        lat = location['latitude']
        lon = location['longitude']
        
        if hasattr(server, 'get_data'):
            method = getattr(server, 'get_data')
            
            if inspect.iscoroutinefunction(method):
                # Async method - await it
                return await method(lat, lon)
            else:
                # Sync method - run in executor to not block
                loop = asyncio.get_event_loop()
                return await loop.run_in_executor(None, method, lat, lon)
        else:
            raise AttributeError(f"Server {server_name} has no get_data method")