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resq_mcp.tools

Drone feed tools for the ResQ MCP server. This module provides simulated drone feed functionality for development and testing. It generates pseudo-random telemetry and analysis data for drone network sectors. The simulation includes:
  • 4 monitored sectors with predefined coordinates
  • Random disaster scenario detection (fire, flood, medical, debris)
  • Swarm status with variable battery and connectivity
  • Drone deployment request handling

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annotations

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random

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UTC

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datetime

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Final

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Coordinates

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DeploymentStatus

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DisasterScenario

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ErrorResponse

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NetworkStatus

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SectorAnalysis

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SectorStatusSummary

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SwarmStatus

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DRONE_SECTORS

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DISASTER_SCENARIOS

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scan_current_sector

def scan_current_sector(
        sector_id: str = "Sector-1") -> SectorAnalysis | ErrorResponse
Scan a specific sector for anomalies using simulated drone sensors. Simulates drone-based surveillance with probabilistic disaster detection. In production, this would integrate with actual drone telemetry and edge AI processing results from the MCP drone feed server. Detection Logic:
  • 30% probability of detecting a disaster scenario per scan
  • Randomly selects from predefined disaster templates
  • Generates NeoFS evidence URL for blockchain submission
  • Returns “clear” status if no anomalies detected
Arguments:
  • sector_id - The sector to scan (“Sector-1” through “Sector-4”). Default is “Sector-1”.
Returns:
  • SectorAnalysis - Complete scan results with detection data and recommended actions if sector exists.
  • ErrorResponse - Error message if sector_id is invalid.
Example:
result = scan_current_sector(“Sector-2”) if isinstance(result, SectorAnalysis): … if result.status == “CRITICAL_ALERT”: … print(f”Alert: {result.detected_object}”) … print(f”Action: {result.recommended_action}”)
Notes: This is a simulation function. Production deployment would replace random detection with actual ML model inference on drone imagery.

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get_all_sectors_status

def get_all_sectors_status() -> NetworkStatus
Get the status of all monitored sectors in the surveillance network. Aggregates scan results across all configured sectors to provide network-wide situational awareness for operator dashboards. Returns:
  • NetworkStatus - Complete network status including:
    • Total sector count
    • Per-sector status summaries (detected objects, confidence)
    • Critical alert count for priority filtering
    • Timestamp of status generation
Example:
status = get_all_sectors_status() print(f”Network: {status.total_sectors} sectors”) print(f”Critical Alerts: {status.critical_alerts}”) for sector_id, summary in status.sectors.items(): … if summary.status == “CRITICAL_ALERT”: … print(f”{sector_id}: {summary.detected_object}”)
Notes: Calls scan_current_sector() for each sector, so inherits its simulation behavior (random detection).

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get_drone_swarm_status

def get_drone_swarm_status() -> SwarmStatus
Get the overall operational status of the drone swarm. Provides fleet-wide health metrics for monitoring drone readiness and availability. Used by operators to assess deployment capacity. Simulation Behavior:
  • Total drones: Fixed at 3 for development
  • Active drones: Random 2-3 (some may be charging/maintenance)
  • Average battery: Random 60-100% (simulated degradation)
  • Network status: Always “operational” in dev mode
Returns:
  • SwarmStatus - Fleet metrics including:
    • Total and active drone counts
    • Fleet-wide average battery percentage
    • Network connectivity status
    • Last sync timestamp (auto-generated)
Example:
swarm = get_drone_swarm_status() if swarm.average_battery < 30: … print(“WARNING: Low fleet battery”) print(f”{swarm.active_drones}/{swarm.total_drones} drones active”)
Notes: Production would aggregate real telemetry from the MCP drone feed server, reporting actual battery, GPS lock, and link quality.

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request_drone_deployment

def request_drone_deployment(
        sector_id: str,
        priority: str = "high") -> DeploymentStatus | ErrorResponse
Request deployment of a drone to a specific sector. Simulates drone dispatch request handling with immediate assignment. In production, this would interface with the drone control module and mission planning system to allocate resources. Simulation Behavior:
  • Assigns random drone unit (UNIT-001 through UNIT-003)
  • Generates random ETA (30-120 seconds)
  • Always returns “deployed” status if sector valid
Arguments:
  • sector_id - The target sector for deployment (e.g., “Sector-1”).
  • priority - Deployment urgency level. Higher priority missions preempt lower priority tasks. Valid values:
    • “low”: Routine surveillance
    • “medium”: Follow-up investigation
    • “high” (default): Active incident response
    • “critical”: Immediate life-threatening situation
Returns:
  • DeploymentStatus - Confirmation with assigned drone and ETA if sector is valid.
  • ErrorResponse - Error message if sector_id is invalid.
Example:
status = request_drone_deployment(“Sector-3”, priority=“critical”) if isinstance(status, DeploymentStatus): … print(f”Drone {status.drone_id} dispatched”) … print(f”ETA: {status.eta_seconds} seconds”)
Notes: Production would check drone availability, battery levels, and weather conditions before confirming deployment.