environmental_summary_by_location
Retrieve environmental data for any U.S. location, including nearby regulated facilities, chemical releases, water quality violations, and hazardous waste sites within a specified radius.
Instructions
Get comprehensive environmental data for a location.
Provides environmental summary including nearby regulated facilities, chemical releases, water quality violations, and hazardous waste sites within a specified radius.
Args: location: Address, city, or ZIP code radius_miles: Search radius in miles (default: 5.0)
Returns: Comprehensive environmental summary
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | ||
| radius_miles | No |
Implementation Reference
- src/tools/location_summary.py:204-220 (handler)The FastMCP @mcp.tool() handler function named 'environmental_summary_by_location' that defines the tool interface and delegates to the core implementation.
async def environmental_summary_by_location( location: str, radius_miles: float = 5.0 ) -> EnvironmentalSummary: """Get comprehensive environmental data for a location. Provides environmental summary including nearby regulated facilities, chemical releases, water quality violations, and hazardous waste sites within a specified radius. Args: location: Address, city, or ZIP code radius_miles: Search radius in miles (default: 5.0) Returns: Comprehensive environmental summary """ return await get_environmental_summary_by_location(location, radius_miles) - src/tools/location_summary.py:20-194 (helper)Core helper function implementing the full tool logic: geocoding, state-based EPA API queries, client-side distance filtering, facility aggregation, ranking, and EnvironmentalSummary formatting.
async def get_environmental_summary_by_location( location: str, radius_miles: float = 5.0 ) -> EnvironmentalSummary: """Get comprehensive environmental data for a location. This tool provides a comprehensive environmental summary for any location in the United States, including nearby regulated facilities, chemical releases, water quality violations, and hazardous waste sites within a specified radius. Args: location: Address, city, or ZIP code (e.g., "New York, NY", "10001", "Los Angeles, CA") radius_miles: Search radius in miles (default: 5.0, max: 100.0) Returns: EnvironmentalSummary containing: - Location coordinates and search parameters - Count of facilities by type (TRI, RCRA, SDWIS, FRS) - Top facilities ranked by distance - Water systems and active violations - Chemical release summary with top chemicals - Hazardous waste sites - Summary statistics Raises: ValueError: If location cannot be geocoded or parameters are invalid Exception: If EPA API queries fail Example: >>> summary = await get_environmental_summary_by_location("10001", 3.0) >>> print(f"Found {summary.total_facilities} facilities") >>> print(f"Active violations: {summary.total_violations}") """ # Validate input parameters if not location or not location.strip(): raise ValueError("Location cannot be empty") if not (0.1 <= radius_miles <= 100.0): raise ValueError("Radius must be between 0.1 and 100.0 miles") location = location.strip() try: logger.info(f"Getting environmental summary for {location} (radius: {radius_miles} miles)") # Step 1: Enhanced geocoding to get coordinates and state try: location_info = await geocode_location(location) coordinates = location_info.coordinates state_code = location_info.state_code logger.info(f"Geocoded {location} to {location_info}") if not state_code: raise ValueError(f"Could not determine state from location '{location}'. Please try a different address, city, or ZIP code.") except ValueError as e: logger.error(f"Geocoding failed for {location}: {e}") raise ValueError(f"Could not find location '{location}'. Please try a different address, city, or ZIP code.") # Step 2: State-based queries (no bounding box needed) logger.info(f"Querying EPA data sources for state {state_code}...") # Initialize EPA API clients async with FRSClient() as frs_client, \ TRIClient() as tri_client, \ SDWISClient() as sdwis_client, \ RCRAClient() as rcra_client: # Execute all state-based queries in parallel results = await asyncio.gather( # FRS facilities frs_client.get_facilities_by_state(state_code, limit=1000), # TRI facilities and releases (use 2022 - latest available) tri_client.get_tri_facilities_by_state(state_code, year=2022, limit=1000), tri_client.get_tri_releases_by_state(state_code, year=2022, limit=1000), # SDWIS water systems (violations disabled due to API issues) sdwis_client.get_water_systems_by_state(state_code, limit=1000), # sdwis_client.get_violations_by_state(state_code, active_only=True, limit=1000), # RCRA hazardous waste sites rcra_client.get_rcra_sites_by_state(state_code, limit=1000), return_exceptions=True ) # Unpack results frs_facilities = results[0] if not isinstance(results[0], Exception) else [] tri_facilities = results[1] if not isinstance(results[1], Exception) else [] tri_releases = results[2] if not isinstance(results[2], Exception) else [] water_systems = results[3] if not isinstance(results[3], Exception) else [] water_violations = [] # Disabled due to API issues rcra_sites = results[4] if not isinstance(results[4], Exception) else [] # Log any errors for i, result in enumerate(results): if isinstance(result, Exception): logger.warning(f"EPA API query {i} failed: {result}") # Step 3: Filter facilities by distance (client-side filtering) logger.info("Filtering facilities by distance...") filtered_frs = filter_by_distance(frs_facilities, coordinates, radius_miles) filtered_tri = filter_by_distance(tri_facilities, coordinates, radius_miles) filtered_rcra = filter_by_distance(rcra_sites, coordinates, radius_miles) # For facilities without coordinates, include them if they're in the same state # (since we queried by state, they're already geographically relevant) for facility in frs_facilities: if facility.coordinates is None and facility.state == state_code: facility.distance_miles = None # Unknown distance if facility not in filtered_frs: filtered_frs.append(facility) for facility in tri_facilities: if facility.coordinates is None and facility.state == state_code: facility.distance_miles = None # Unknown distance if facility not in filtered_tri: filtered_tri.append(facility) for facility in rcra_sites: if facility.coordinates is None and facility.state == state_code: facility.distance_miles = None # Unknown distance if facility not in filtered_rcra: filtered_rcra.append(facility) # Filter water systems by distance filtered_water_systems = [] for system in water_systems: if system.coordinates: from ..utils.distance import haversine_distance distance = haversine_distance(coordinates, system.coordinates) if distance <= radius_miles: system.distance_miles = distance filtered_water_systems.append(system) elif system.state == state_code: # Include water systems without coordinates if in same state system.distance_miles = None filtered_water_systems.append(system) # Step 4: Aggregate facilities logger.info("Aggregating facility data...") all_facilities = aggregate_facilities( filtered_frs, filtered_tri, filtered_rcra, filtered_water_systems ) # Step 5: Rank facilities by distance (top 50) top_facilities = rank_facilities(all_facilities, limit=50) # Step 6: Build environmental summary logger.info("Building environmental summary...") summary = format_environmental_summary( location=location, coordinates=coordinates, radius_miles=radius_miles, facilities=top_facilities, water_systems=filtered_water_systems, water_violations=water_violations, chemical_releases=tri_releases, hazardous_sites=filtered_rcra ) logger.info(f"Environmental summary complete: {summary.total_facilities} facilities, " f"{summary.total_violations} violations, {summary.total_hazardous_sites} hazardous sites") return summary except ValueError: # Re-raise validation errors raise except Exception as e: logger.error(f"Failed to get environmental summary for {location}: {e}") raise Exception(f"Failed to retrieve environmental data for {location}: {e}") - src/models/summary.py:12-53 (schema)Pydantic BaseModel defining the output schema returned by the tool, including location info, facility counts, top facilities, water violations, chemical releases, hazardous sites, and summary stats.
class EnvironmentalSummary(BaseModel): """Comprehensive environmental data summary for a location.""" # Location information location: str = Field(..., description="Original location query") coordinates: Optional[Coordinates] = Field(None, description="Resolved coordinates") radius_miles: float = Field(..., description="Search radius used") # Facility counts by type facility_counts: Dict[str, int] = Field(default_factory=dict, description="Count of facilities by type") total_facilities: int = Field(default=0, ge=0, description="Total facilities found") # Top facilities (ranked by distance) top_facilities: List[FacilityInfo] = Field(default_factory=list, description="Top facilities within radius") # Water system information water_systems: List[WaterSystem] = Field(default_factory=list, description="Water systems in area") water_violations: List[WaterViolation] = Field(default_factory=list, description="Active water violations") total_violations: int = Field(default=0, ge=0, description="Total active violations") # Chemical release information chemical_releases: ReleaseSummary = Field(default_factory=lambda: ReleaseSummary( total_facilities=0, total_chemicals=0, total_releases=0.0, reporting_year=2023 ), description="Chemical release summary") # Hazardous waste sites hazardous_sites: List[FacilityInfo] = Field(default_factory=list, description="RCRA hazardous waste sites") total_hazardous_sites: int = Field(default=0, ge=0, description="Total hazardous waste sites") # Summary statistics summary_stats: Dict[str, str | int | float | bool] = Field(default_factory=dict, description="Additional summary statistics") # Metadata query_timestamp: Optional[str] = Field(None, description="When the query was executed") data_sources: List[str] = Field(default_factory=list, description="EPA data sources queried") def __str__(self) -> str: return f"Environmental Summary for {self.location} ({self.total_facilities} facilities)" - src/models/common.py:52-65 (schema)Pydantic BaseModel defining input parameters matching the tool signature (location: str, radius_miles: float), with validation.
class LocationParams(BaseModel): """Parameters for location-based queries.""" location: str = Field(..., min_length=1, max_length=200, description="Address, city, or ZIP code") radius_miles: float = Field(default=5.0, ge=0.1, le=100.0, description="Search radius in miles") @field_validator('location') @classmethod def validate_location(cls, v): """Basic validation for location string.""" if not v.strip(): raise ValueError('Location cannot be empty') return v.strip() - src/tools/location_summary.py:197-221 (registration)Registration function that defines and registers the MCP tool using @mcp.tool() decorator, called from server.py.
def register_tool(mcp: FastMCP): """Register the environmental summary tool with FastMCP. Args: mcp: FastMCP instance """ @mcp.tool() async def environmental_summary_by_location( location: str, radius_miles: float = 5.0 ) -> EnvironmentalSummary: """Get comprehensive environmental data for a location. Provides environmental summary including nearby regulated facilities, chemical releases, water quality violations, and hazardous waste sites within a specified radius. Args: location: Address, city, or ZIP code radius_miles: Search radius in miles (default: 5.0) Returns: Comprehensive environmental summary """ return await get_environmental_summary_by_location(location, radius_miles)