Spaces:
Sleeping
Sleeping
File size: 17,167 Bytes
7b6b271 23a9367 7b6b271 23a9367 7b6b271 23a9367 7b6b271 23a9367 7b6b271 35e0eb7 7b6b271 35e0eb7 7b6b271 35e0eb7 7b6b271 23a9367 7b6b271 23a9367 7b6b271 23a9367 7b6b271 23a9367 7b6b271 23a9367 7b6b271 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 |
"""
Main Surf Spot Finder Tool - Complete Workflow Orchestration.
This module provides the primary MCP tool for finding and ranking surf spots.
It orchestrates the complete workflow from user input to AI-powered recommendations:
1. Location resolution (address/coordinates)
2. Nearby spot discovery (distance filtering)
3. Real-time condition analysis (wave/wind data)
4. Multi-factor evaluation and scoring
5. AI-powered reasoning and explanations
The tool integrates multiple data sources:
- MCP resources for surf spot database
- Stormglass API for marine conditions
- Nominatim for geocoding
- LLM providers for natural language reasoning
Example:
>>> finder = SurfSpotFinder()
>>> input_data = SpotFinderInput(
... user_location="Málaga, Spain",
... max_distance_km=50,
... top_n=3,
... user_preferences={"skill_level": "intermediate"}
... )
>>> result = await finder.run(input_data)
>>> print(f"Found {len(result.spots)} spots")
Author: Surf Spot Finder Team
License: MIT
"""
import json
import os
from typing import Dict, Any, List, Optional
from pydantic import BaseModel, Field
from geopy.distance import geodesic
import asyncio
import logging
from .location_tool import LocationTool, LocationInput
from .stormglass_tool import create_stormglass_tool
from .surf_eval_tool import SurfEvaluatorTool
from .llm_agent_tool import SurfLLMAgent, LLMAgentInput
logger = logging.getLogger(__name__)
class SpotFinderInput(BaseModel):
"""Input schema for the surf spot finder tool.
Attributes:
user_location: User's location as address, city name, or coordinates.
max_distance_km: Maximum search radius in kilometers (default: 50).
top_n: Number of top-ranked spots to return (default: 3).
user_preferences: Dict containing skill_level, board_type, etc.
"""
user_location: str = Field(description="User's location (name, address, or coordinates)")
max_distance_km: float = Field(default=50, description="Maximum distance to search for spots (km)")
top_n: int = Field(default=3, description="Number of top spots to return")
user_preferences: Dict[str, Any] = Field(default_factory=dict, description="User surfing preferences")
class SpotFinderOutput(BaseModel):
"""Output schema for surf spot finder results.
Attributes:
success: Whether the operation completed successfully.
user_location: Resolved coordinates as {"lat": float, "lon": float}.
spots: List of ranked surf spots with scores and conditions.
ai_summary: Brief AI-generated summary of recommendations.
ai_reasoning: Detailed AI analysis and explanations.
error: Error message if success is False.
"""
success: bool
user_location: Optional[Dict[str, float]] = None
spots: List[Dict[str, Any]] = []
ai_summary: str = ""
ai_reasoning: str = ""
error: str = ""
class SurfSpotFinder:
"""Main orchestration tool for finding optimal surf spots.
This class coordinates the complete surf recommendation workflow by
integrating location services, marine data APIs, evaluation algorithms,
and AI reasoning to provide ranked surf spot recommendations.
The workflow:
1. Resolves user location to coordinates
2. Filters surf spots by distance
3. Fetches real-time wave conditions
4. Evaluates each spot using multi-factor scoring
5. Generates AI-powered analysis and explanations
Attributes:
name: Tool identifier for MCP registration.
description: Human-readable tool description.
location_tool: Service for address/coordinate resolution.
stormglass_tool: API client for marine condition data.
evaluator: Surf condition evaluation algorithm.
llm_agent: AI reasoning and natural language generation.
spots_db: Cached surf spot database from MCP resources.
Example:
>>> finder = SurfSpotFinder()
>>> input_data = SpotFinderInput(user_location="Lisbon")
>>> result = await finder.run(input_data)
>>> print(f"Best spot: {result.spots[0]['name']}")
"""
name = "find_surf_spots"
description = "Find and rank the best surf spots near a given location based on current conditions"
def __init__(self):
self.location_tool = LocationTool()
self.stormglass_tool = create_stormglass_tool()["function"]
self.evaluator = SurfEvaluatorTool()
self.llm_agent = SurfLLMAgent()
self.spots_db = self._load_surf_spots()
def _load_surf_spots(self) -> List[Dict[str, Any]]:
"""
Load surf spots database via MCP resources with fallback.
Attempts to load surf spots using MCP resource primitive first,
then falls back to direct file I/O if MCP resources are unavailable.
This ensures reliability across different execution contexts.
MCP Resource Path: surf://spots/database
Fallback Path: ../data/surf_spots.json
Returns:
List[Dict[str, Any]]: List of surf spot dictionaries
Note:
Import is done locally to avoid circular dependency issues.
Uses fallback to file I/O if async context is unavailable.
"""
try:
# Try MCP resources first (preferred method)
import asyncio
# Local import to avoid circular dependency issues
try:
from mcp_server.mcp_server import read_resource
except ImportError:
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
from mcp_server import read_resource
# Check if we're already in an async context
try:
loop = asyncio.get_running_loop()
# Already in async context - can't create new loop
# Fall back to file I/O
raise RuntimeError("Already in async context, using fallback")
except RuntimeError:
# Not in async context - safe to create new loop
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
# Read from MCP resource
content = loop.run_until_complete(read_resource("surf://spots/database"))
loop.close()
data = json.loads(content)
spots = data.get("spots", [])
logger.info(f"✅ Loaded {len(spots)} surf spots via MCP resources")
return spots
except Exception as e:
# MCP resources unavailable - use fallback
logger.warning(f"⚠️ MCP resources not available in this context, using file I/O fallback")
# Fallback: Direct file I/O for reliability
try:
spots_path = os.path.join(os.path.dirname(__file__), "..", "data", "surf_spots.json")
with open(spots_path, 'r') as f:
data = json.load(f)
spots = data["spots"]
logger.info(f"✅ Loaded {len(spots)} surf spots via fallback file I/O")
return spots
except Exception as fallback_error:
# Total failure - return empty list to prevent crash
logger.error(f"❌ Both MCP and fallback failed: {fallback_error}")
return []
def find_nearby_spots(self, user_lat: float, user_lon: float, max_distance_km: float) -> List[Dict[str, Any]]:
"""Find surf spots within specified distance of user location.
Uses haversine formula to calculate spherical distances between
user coordinates and each surf spot in the database.
Args:
user_lat: User's latitude in decimal degrees.
user_lon: User's longitude in decimal degrees.
max_distance_km: Maximum search radius in kilometers.
Returns:
List of surf spots within radius, sorted by distance.
Each spot includes original data plus distance_km field.
"""
nearby_spots = []
user_location = (user_lat, user_lon)
for spot in self.spots_db:
spot_location = (spot["latitude"], spot["longitude"])
distance = geodesic(user_location, spot_location).kilometers
if distance <= max_distance_km:
spot_with_distance = spot.copy()
spot_with_distance["distance_km"] = round(distance, 1)
nearby_spots.append(spot_with_distance)
# Sort by distance
nearby_spots.sort(key=lambda x: x["distance_km"])
return nearby_spots
async def get_spot_conditions(self, spot: Dict[str, Any]) -> Optional[Dict[str, Any]]:
"""Get current wave conditions for a specific surf spot.
Fetches real-time marine data including wave height, direction,
period, wind speed/direction, and tide information.
Args:
spot: Surf spot dictionary with latitude/longitude.
Returns:
Dict containing current conditions, or None if fetch fails.
Includes wave_height, wave_direction, wind_speed, etc.
"""
try:
# Use the Stormglass tool to get wave data
from .stormglass_tool import WaveDataInput
wave_input = WaveDataInput(lat=spot["latitude"], lon=spot["longitude"])
conditions = self.stormglass_tool(wave_input)
# Convert to dict for compatibility
if hasattr(conditions, 'dict'):
return conditions.dict()
elif hasattr(conditions, '__dict__'):
return conditions.__dict__
return conditions
except Exception as e:
logger.error(f"Failed to get conditions for {spot['name']}: {e}")
return None
async def evaluate_spot(self, spot: Dict[str, Any], conditions: Dict[str, Any], user_prefs: Dict[str, Any]) -> Dict[str, Any]:
"""Evaluate a single surf spot using current conditions and user preferences.
Applies multi-factor scoring algorithm considering wave conditions,
wind analysis, swell direction, and skill compatibility.
Args:
spot: Surf spot data including location and characteristics.
conditions: Current wave/wind conditions from marine APIs.
user_prefs: User preferences including skill level, board type.
Returns:
Dict containing evaluated spot with score, explanation, and
breakdown of individual scoring factors.
"""
evaluation = await self.evaluator.run({
"spot": spot,
"conditions": conditions,
"prefs": user_prefs
})
return {
"id": spot["id"],
"name": spot["name"],
"location": f"{spot['location']}, {spot['region']}",
"latitude": spot["latitude"],
"longitude": spot["longitude"],
"distance_km": spot["distance_km"],
"score": evaluation["score"],
"explanation": evaluation["explanation"],
"breakdown": evaluation["breakdown"],
"conditions": conditions,
"characteristics": spot["characteristics"]
}
async def run(self, input_data: SpotFinderInput) -> SpotFinderOutput:
"""Execute the complete surf spot finding workflow.
This is the main entry point that orchestrates all steps of the
surf recommendation process from user input to final rankings.
Args:
input_data: User request containing location, preferences, and filters.
Returns:
Complete results including ranked spots, AI analysis, and metadata.
Raises:
No exceptions - all errors are captured in SpotFinderOutput.error
for graceful degradation and user-friendly error messages.
"""
try:
# Step 1: Resolve user location
logger.info(f"Resolving location: {input_data.user_location}")
location_result = self.location_tool.run(
LocationInput(location_query=input_data.user_location)
)
if not location_result.success:
return SpotFinderOutput(
success=False,
error=f"Could not resolve location: {location_result.error}"
)
user_coords = location_result.coordinates
user_lat, user_lon = user_coords["lat"], user_coords["lon"]
logger.info(f"User location resolved to: {user_lat}, {user_lon}")
# Step 2: Find nearby surf spots
nearby_spots = self.find_nearby_spots(user_lat, user_lon, input_data.max_distance_km)
if not nearby_spots:
return SpotFinderOutput(
success=False,
error=f"No surf spots found within {input_data.max_distance_km}km of {input_data.user_location}"
)
logger.info(f"Found {len(nearby_spots)} nearby spots")
# Step 3: Get conditions and evaluate each spot
evaluated_spots = []
# Process spots concurrently for speed
semaphore = asyncio.Semaphore(5) # Limit concurrent API calls
async def process_spot(spot):
async with semaphore:
conditions = await self.get_spot_conditions(spot)
if conditions:
return await self.evaluate_spot(spot, conditions, input_data.user_preferences)
return None
tasks = [process_spot(spot) for spot in nearby_spots]
results = await asyncio.gather(*tasks, return_exceptions=True)
for result in results:
if isinstance(result, Exception):
logger.error(f"Spot evaluation failed: {result}")
elif result:
evaluated_spots.append(result)
# Step 4: Sort by score and return top N
evaluated_spots.sort(key=lambda x: x["score"], reverse=True)
top_spots = evaluated_spots[:input_data.top_n]
# Step 5: Generate AI reasoning and summary
ai_summary = ""
ai_reasoning = ""
try:
logger.info("Generating AI analysis of surf conditions...")
llm_result = await self.llm_agent.run(LLMAgentInput(
user_location=input_data.user_location,
user_preferences=input_data.user_preferences,
surf_spots=top_spots
))
if llm_result.success:
ai_summary = llm_result.summary
ai_reasoning = llm_result.reasoning
logger.info("AI analysis completed successfully")
else:
logger.warning(f"LLM agent failed: {llm_result.error}")
ai_summary = f"Found {len(top_spots)} surf spots. Top recommendation: {top_spots[0]['name']} with {top_spots[0]['score']}/100 score."
except Exception as e:
logger.error(f"AI analysis failed: {e}")
ai_summary = f"Found {len(top_spots)} surf spots near {input_data.user_location}. Analysis based on wave conditions and user preferences."
return SpotFinderOutput(
success=True,
user_location=user_coords,
spots=top_spots,
ai_summary=ai_summary,
ai_reasoning=ai_reasoning
)
except Exception as e:
logger.error(f"Surf spot finder error: {e}")
return SpotFinderOutput(
success=False,
error=f"Internal error: {str(e)}"
)
def create_spot_finder_tool() -> Dict[str, Any]:
"""Factory function to create the surf spot finder tool.
Creates and configures the main MCP tool for surf spot recommendations.
Returns tool specification compatible with MCP protocol.
Returns:
Dict containing tool name, description, schema, and function reference.
Example:
>>> tool = create_spot_finder_tool()
>>> result = await tool["function"](input_data)
"""
tool = SurfSpotFinder()
return {
"name": tool.name,
"description": tool.description,
"input_schema": SpotFinderInput.schema(),
"function": tool.run,
} |