A production-ready Model Context Protocol (MCP) server that connects AI agents to the ResQ platform's robotics, physics simulations, and disaster telemetry.
resq-mcp allows AI agents (Claude Desktop, VS Code Copilot, Cursor, etc.) to command and monitor disaster response operations through a secure, typed interface.
- Drone Fleet Command: Real-time telemetry, sector scanning, and autonomous swarm deployment via the Hybrid Coordination Engine (HCE).
- Predictive Intelligence: Probabilistic disaster forecasting and sector-level vulnerability mapping (PDIE).
- Digital Twin Simulations: Physics-based RL optimization strategies for incident response (DTSOP).
- Safe-Mode Execution: Built-in protection prevents destructive platform mutations in production environments by default.
Run the server instantly without manual cloning using uvx. Add this to your claude_desktop_config.json:
{
"mcpServers": {
"resq": {
"command": "uvx",
"args": ["resq-mcp"],
"env": {
"RESQ_API_KEY": "your-prod-token",
"RESQ_SAFE_MODE": "true"
}
}
}
}A pre-configured .vscode/mcp.json is included in this repo. To enable it:
# From the project root — starts the MCP server for VS Code / Cursor
code .Or create .vscode/mcp.json manually in any workspace:
{
"servers": {
"resq-mcp": {
"type": "stdio",
"command": "uv",
"args": ["run", "resq-mcp"],
"env": {
"RESQ_API_KEY": "resq-dev-token",
"RESQ_SAFE_MODE": "true"
}
}
}
}Tip: For
uvx(no local clone needed), replace"command": "uv"and"args"with"command": "uvx"and"args": ["resq-mcp"].
Set up the local environment using uv:
git clone https://github.com/resq-software/mcp.git
cd mcp
uv sync
uv run resq-mcpThe server acts as a secure intermediary, translating natural language requests into authenticated, platform-native service calls.
C4Context
title ResQ MCP Integration
Person(ai, "AI Client", "Claude / VS Code / Cursor")
System_Boundary(resq_boundary, "resq-mcp Server") {
System(server, "resq-mcp Server", "FastMCP Interface")
System_Boundary(backend, "ResQ Platform") {
Component(dtsop, "DTSOP Engine", "Physics/RL Simulations")
Component(hce, "HCE Engine", "Coordination Logic")
Component(telemetry, "Drone Telemetry", "Real-time Status")
}
}
Rel(ai, server, "Uses MCP (STDIO/SSE)")
Rel(server, dtsop, "Executes Simulation")
Rel(server, hce, "Validates Incidents")
Rel(server, telemetry, "Subscribes to Data")
src/resq_mcp/
├── server.py # FastMCP init, lifespan, background tasks
├── resources.py # @mcp.resource() endpoints (drones, sims)
├── prompts.py # @mcp.prompt() templates (incident response)
├── core/ # Cross-cutting: config, errors, security, telemetry, timeout
├── drone/ # Drone feed: scan, swarm, deployment (models + service)
├── dtsop/ # Digital Twin: simulation, optimization (models + service + tools)
├── hce/ # Hybrid Coordination: incidents, missions (models + service + tools)
└── pdie/ # Predictive Intelligence: vulnerability, alerts (models + service)
Control server behavior via environment variables or a .env file:
| Variable | Description | Default |
|---|---|---|
RESQ_API_KEY |
Platform authentication token | resq-dev-token |
RESQ_SAFE_MODE |
Prevents destructive mutations | true |
RESQ_PORT |
Port for SSE (networked) mode | 8000 |
RESQ_HOST |
Host to bind the SSE server | 0.0.0.0 |
RESQ_DEBUG |
Enable verbose logging | false |
Safe Mode is enabled by default (RESQ_SAFE_MODE=true). In this state, any tool that performs platform mutations (e.g., dispatching a drone swarm or starting a high-fidelity simulation) will raise a FastMCPError. This allows AI agents to plan missions safely without triggering real-world consequences. Disable this only when you are ready for autonomous execution.
| Tool | Description |
|---|---|
validate_incident |
Submit a confirmation or rejection for an incident report. Supports idempotent re-submission and conflict detection for opposing validations. |
update_mission_params |
Push authorized mission parameters to a specific drone for an approved strategy. Includes urgency escalation, conflict guards, and idempotent re-dispatch. |
| Tool | Description |
|---|---|
run_simulation |
Queue a high-fidelity Digital Twin physics simulation (flood, wildfire, earthquake). Returns a job ID — subscribe to the resource URI for progress updates. |
get_deployment_strategy |
Generate an RL-optimized drone deployment and evacuation strategy for a confirmed incident or PDIE pre-alert. |
| URI | Description |
|---|---|
resq://drones/active |
Real-time fleet status — drone types, battery levels, sector assignments. |
resq://simulations/{sim_id} |
Simulation progress and results. Supports SSE subscriptions for push updates on state transitions. |
| Prompt | Description |
|---|---|
incident_response_plan |
Structured crisis coordination template that guides an AI agent through situation analysis, asset allocation, and risk assessment for a given incident. |
You: "Run a flood simulation for Sector-3 with water level 4.2m"
Agent: Calls run_simulation → receives SIM-ABCD1234
Subscribes to resq://simulations/SIM-ABCD1234
Waits for status: completed
Returns result URL and analysis
You: "Validate incident INC-789 and deploy drones"
Agent: 1. Calls validate_incident(INC-789, confirmed=True)
2. Calls get_deployment_strategy("INC-789")
3. Reviews strategy with operator
4. Calls update_mission_params("DRONE-Alpha", "STRAT-XYZ", urgent=True)
5. Returns mission parameters and audit hash
You: Use the incident_response_plan prompt for INC-456
Agent: Receives structured template → calls tools → produces:
- Situation Summary
- Asset Allocation
- Risk Assessment
We use uv for dependency management and ruff for linting.
- Setup:
uv sync(installs all dependencies including dev group). - Test:
uv run pytest - Lint:
uv run ruff check .
Distributed under the Apache-2.0 License. Copyright 2026 ResQ.