resQ MCP Server
The resQ MCP Server connects AI agents to a disaster response platform, enabling three core capabilities:
Run Digital Twin Simulations (
run_simulation): Trigger high-fidelity physics-based disaster scenario simulations (flood, wildfire, earthquake) for a given geographic sector, with custom parameters (e.g., wind speed, water level) and priority level (standard/urgent). Returns a simulation ID and subscription URI for real-time progress monitoring, with results stored on NeoFS/IPFS for audit trails.Get RL-Optimized Deployment Strategies (
get_deployment_strategy): Generate reinforcement learning-based drone deployment and evacuation strategies for a given incident or pre-alert. Returns recommended drone type counts, prioritized evacuation routes, an estimated success rate, and a simulation proof URL.Validate Incidents (
validate_incident): Submit confirmation or rejection of incident reports within the Hybrid Coordination Engine (HCE) workflow. Human operators or automated systems can confirm/reject incidents, link them to pre-alerts, and add reasoning notes — triggering full responses or logging false positives for ML model refinement.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@resQ MCP ServerRun a simulation and provide a drone deployment strategy for the North Zone fire"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ResQ PyPI Packages
Python packages for the ResQ disaster response platform, published to PyPI under the resq-software organization.
Packages
Package | Description | Version |
FastMCP server -- connects AI agents to drone fleet, simulations, and disaster intelligence | ||
Zero-dependency data structures & algorithms for search, rescue, and geospatial ops |
Architecture
graph TB
subgraph "resq-software/pypi"
subgraph "packages/resq-mcp"
MCP[resq-mcp<br/><i>FastMCP Server</i>]
DTSOP[DTSOP<br/>Digital Twin Simulations]
HCE[HCE<br/>Hybrid Coordination]
PDIE[PDIE<br/>Predictive Intelligence]
DRONE[Drone Fleet<br/>Telemetry & Control]
MCP --> DTSOP
MCP --> HCE
MCP --> PDIE
MCP --> DRONE
end
subgraph "packages/resq-dsa"
DSA[resq-dsa<br/><i>Zero-Dep DSA</i>]
BF[BloomFilter]
CMS[CountMinSketch]
GR[Graph + A*]
HP[BoundedHeap]
TR[Trie]
DSA --> BF
DSA --> CMS
DSA --> GR
DSA --> HP
DSA --> TR
end
end
AI[AI Clients<br/>Claude / VS Code / Cursor] -->|MCP protocol| MCP
APP[Python Applications] -->|pip install| DSAQuick Start
# Install a package
pip install resq-mcp # MCP server for AI agents
pip install resq-dsa # Data structures (zero dependencies)Development
# Clone and setup
git clone https://github.com/resq-software/pypi.git && cd pypi
./bootstrap.sh
# Work on a package
cd packages/resq-mcp && uv sync && uv run pytest
cd packages/resq-dsa && uv sync && uv run pytestRelease Flow
graph LR
PUSH[Push to main] --> SR[Semantic Release]
SR -->|feat: / fix:| BUMP[Version Bump + Changelog]
BUMP --> BUILD[Build sdist + wheel]
BUILD --> ATTEST[Sigstore Attestation]
ATTEST --> PYPI[Publish to PyPI]
PYPI --> DOCKER[Docker Image<br/><i>resq-mcp only</i>]Both packages use python-semantic-release with Trusted Publisher OIDC. Conventional commits on main automatically version, changelog, and publish.
License
Apache-2.0 -- Copyright 2025 ResQ Software
Maintenance
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