Seattle Fire Department MCP Server
Enables real-time monitoring of Seattle Fire Department incident data, allowing users to check for active fires, evacuation orders, and retrieve live emergency response information through natural language queries.
README
MCP SFD - Seattle Fire Department MCP Server
A Model Context Protocol (MCP) server that provides tools for LLMs to fetch and analyze Seattle Fire Department live incident data.
Features
- Low-level API proxy (
sfd.fetch_raw) with normalization and caching - Latest incident retrieval (
sfd.latest_incident) for quick updates - Fire detection (
sfd.is_fire_active) with intelligent status analysis - Evacuation monitoring (
sfd.has_evacuation_orders) with keyword scanning - Robust error handling and retry logic
- Comprehensive data normalization
- In-memory caching with configurable TTL
Installation
# Install the package
pip install -e .
# Install development dependencies
pip install -e ".[dev]"
Usage
Running the MCP Server
# Run with default settings
python -m mcp_sfd.server
# Or use the CLI command
mcp-sfd
Environment Variables
SFD_BASE_URL: Base URL for SFD API (default:https://sfdlive.com/api/data/)DEFAULT_CACHE_TTL: Default cache TTL in seconds (default: 15)
Available Tools
sfd.fetch_raw
Low-level proxy for the SFD API with full parameter control.
{
"order": "new",
"length": 100,
"search": "Any",
"cacheTtlSeconds": 15
}
sfd.latest_incident
Returns the single most recent incident.
{}
sfd.is_fire_active
Checks if there are any active fire incidents in Seattle.
{
"lookbackMinutes": 120
}
sfd.has_evacuation_orders
Scans for evacuation-related keywords in recent incidents.
{
"lookbackMinutes": 180
}
Development
Running Tests
# Run all tests
pytest
# Run with coverage
pytest --cov=mcp_sfd
# Run specific test file
pytest tests/test_normalize.py
Code Quality
# Format code
black mcp_sfd/ tests/
# Lint
ruff check mcp_sfd/ tests/
# Type check
mypy mcp_sfd/
Architecture
The server is built with several key components:
- HTTP Client (
http_client.py): Handles API requests with retry logic and caching - Data Normalization (
normalize.py): Converts upstream API format to standardized schemas - Pydantic Schemas (
schemas.py): Type-safe data models for all inputs and outputs - Tool Implementations (
tools/): Individual MCP tool logic - Server (
server.py): MCP server registration and error handling
Data Normalization
The server handles complex data transformations:
- Flattens nested upstream data structures
- Converts Seattle local time to UTC
- Normalizes coordinates from various formats
- Parses unit identifiers and status information
- Standardizes boolean fields
Error Handling
All tools use standardized MCP error codes:
UPSTREAM_HTTP_ERROR: API connectivity issuesUPSTREAM_TIMEOUT: Request timeoutsSCHEMA_VALIDATION_ERROR: Data parsing failures
License
MIT
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