Geospatial MCP Build — Open Data MCP, Drought Analysis & FloodGuard Agent

A hands-on exploration of geospatial AI built with Kiro. This repo brings together three things that build on each other:

1. OpenDataMCP — a custom FastMCP server that turns the AWS Registry of Open Data (RODA) and public STAC catalogs into agent-callable tools (dataset search, STAC queries, and NDVI/NDBI/land-cover analysis on Sentinel-2 imagery).

2. A drought analysis script — a standalone NDVI comparison of Shasta Lake, CA between a recovery year (2017) and an extreme drought year (2021).

3. FloodGuard — a geospatially-aware flood-insurance claims agent built on the Strands Agents SDK and deployed to Amazon Bedrock AgentCore, plus the Geospatial Kiro Power Pack used to give Kiro itself geospatial superpowers.

If you want the story of how this was built — the prompts, the tools added, the agent powers wired up — see INTERACTIONS.md. For the verbatim lab instructions and prompts, see GEOSPATIAL_MCP_BUILD_LABS.md.

Repository layout

.
├── open_data_mcp.py              # FastMCP server: RODA + STAC + NDVI/NDBI tools
├── drought_analysis.py           # Shasta Lake NDVI drought comparison (2017 vs 2021)
├── ndvi_shasta_*.tif             # NDVI rasters + difference GeoTIFFs (outputs)
├── shasta_*_ndvi_classified.*    # Land-cover classification outputs (PNG + GeoTIFF)
├── shasta_drought_ndvi_comparison.png
│
├── floodguard/                   # Flood-claims agent on Amazon Bedrock AgentCore
│   ├── app/floodguard/           # Strands agent + self-contained flood tools
│   ├── agentcore/                # AgentCore config + CDK deployment
│   ├── DEPLOYMENT.md             # Full, reproducible build + deploy record
│   └── maui_demo_prompt.txt      # Demo scenario (Maui 2026 flooding)
│
└── sample-geospatial-kiro-power-pack/   # The Geospatial Kiro Power (MCP servers + skills)

1. OpenDataMCP server

open_data_mcp.py is a single-file MCP server. It fetches the RODA NDJSON index once (cached for an hour), then exposes discovery and analysis tools over stdio. Imagery is read cloud-natively via COG byte-range reads — no bulk downloads.

Tools

Run it

# Dependencies: fastmcp, httpx, numpy, rasterio, pyproj, scipy, matplotlib
uv run python open_data_mcp.py     # serves over stdio

Register it with Kiro (MCP)

Add it to your workspace MCP config at .kiro/settings/mcp.json:

{
  "mcpServers": {
    "OpenDataMCP": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/repo", "open_data_mcp.py"],
      "disabled": false,
      "autoApprove": ["search_datasets", "search_datasets_by_tags", "get_dataset_info"]
    }
  }
}

2. Drought analysis (Shasta Lake)

drought_analysis.py loads two pre-computed NDVI rasters (2017 recovery vs 2021 extreme drought), computes per-class vegetation statistics, writes a difference raster, and renders a three-panel comparison figure.

uv run python drought_analysis.py
# → shasta_drought_ndvi_comparison.png
# → ndvi_shasta_diff_2021_vs_2017.tif

The report quantifies the drought signal: lower mean NDVI in 2021, a rise in the water/bare-soil class as the lake receded, and dense-vegetation loss on the surrounding hillsides.

3. FloodGuard agent (Amazon Bedrock AgentCore)

floodguard/ is a demo AI support chatbot for a fictional flood-insurance agency. It is geospatially aware, understands Sentinel-2 (optical, NDWI) and Sentinel-1 (SAR VV backscatter) imagery, and triages flood claims from a before/after scene pair.

Highlights:

• Built with the Strands Agents SDK, wrapped by BedrockAgentCoreApp.

• Seven self-contained tools (geocode_place, search_flood_scenes, analyze_flood_change, analyze_sar_flood, assess_flood_claim, …) so the agent behaves identically locally and in the cloud.

• Optional OpenDataMCP integration over stdio in local dev (set OPEN_DATA_MCP_PATH).

• Deployed via AWS CDK to AgentCore Runtime (CodeZip, ARM64, Python 3.12).

Full, reproducible build and deploy steps — including the Maui 2026 flooding demo — are in floodguard/DEPLOYMENT.md.

Security note: the reference deployment uses networkMode: PUBLIC. Access is still gated by IAM SigV4 (no unauthenticated calls), but for production you should scope invoke permissions to specific principals and consider a JWT authorizer or VPC networking.

4. Geospatial Kiro Power Pack

sample-geospatial-kiro-power-pack/ is a modular Kiro Power that gives Kiro unified access to the fragmented geospatial landscape. It is organized around a dual-fragmentation framing:

• Pillar A — data access: geo-stac, geo-vector, geo-geocode-route, geo-terrain, geo-weather-climate, geo-biodiversity, geo-ogc.

• Pillar B — processing/compute: geo-ops, geo-formats, geo-query, geo-raster, geo-pointcloud, geo-index, geo-3d, geo-warehouse.

• Pillar C — GeoAI: geo-foundation-models (Clay, Prithvi, SatCLIP, SAMGeo), geo-embedding-search.

• Plus a hub (kiro-geospatial), a shared base (geo-common), commercial imagery, and an aws-geo-compute peer power.

It also ships skills (COG/GeoParquet guidance, CRS handling, tool selection, spatial SQL) and steering workflows (COG conversion, zonal statistics, STAC discover→analyze, embedding change detection). See its own bundle-manifest.json and examples/ for details.

Install it as a Kiro Power

Kiro Powers can be installed straight from GitHub repos. To add the geospatial power on the remote machine where you are running Kiro:

1. Copy this link and paste it into your browser on the remote machine (where Kiro runs): https://github.com/aws-samples/sample-geospatial-kiro-power-pack

2. Open a new terminal in the workshop folder, clone the repository, and change into it:

   git clone https://github.com/aws-samples/sample-geospatial-kiro-power-pack.git
   cd ./sample-geospatial-kiro-power-pack/

3. Follow the Installation instructions from the repository and install all of the MCP servers listed, then configure them. You do not need to create a new uv environment — you are already in one that is activated.

Your existing MCP servers are left untouched. Once installation completes, you should have the full set of geospatial powers (the hub, data-access, processing, and GeoAI servers listed above) available in Kiro.

Key takeaways

• MCP turns open geospatial data into agent tools. A single small server (open_data_mcp.py) makes 1,000+ RODA datasets and public STAC catalogs directly callable by an AI agent.

• Cloud-native reads beat downloads. Every raster operation here uses COG byte-range reads over HTTPS against public, credential-free buckets — fast enough to run inside an AgentCore microVM.

• Portability by design. FloodGuard's tools are self-contained so the agent behaves the same in local dev and when deployed; the MCP server is an optional local enhancement, not a runtime dependency.

• Same science, two indices, two sensors. NDVI/NDWI/NDBI are simple band-ratio indices; swapping bands (and using SAR when it's cloudy) covers vegetation, water, and built-up analysis with one mental model.

• Powers scale Kiro's reach. The Geospatial Power Pack packages dozens of specialized MCP servers behind one credential surface, with skills and steering that teach Kiro when to use each one.

Prerequisites

• Python 3.10+ (3.12 recommended) and uv

• Node.js 20+ (for the AgentCore CDK deploy path)

• AWS credentials + Bedrock model access (only for deploying FloodGuard)

License

See the individual subprojects for their licenses (the Power Pack includes its own LICENSE and NOTICE).