RMS Location Intelligence MCP Server

MCP server providing access to the Moody's RMS Location Intelligence API for property catastrophe risk analysis.

Overview

This server wraps the RMS Location Intelligence API, offering 114+ data product layers covering:

• Earthquake (eq)

• Windstorm (ws)

• Flood (fl)

• Wildfire (wf)

• Convective storm (cs)

• Winter storm (wt)

• Terrorism (tr)

Coverage spans 20+ regions worldwide including US, EU, CA, AU, CN, JP, and more.

Related MCP server: DeepMap AI MCP Server

Project Structure

C:/Users/khano3/li-rms-mcp/
├── server.py          # Main FastMCP server (5 tools, 659 lines)
├── layers.py          # Layer catalog (114 layers, 210 lines)
├── requirements.txt   # Python dependencies
├── .env               # API credentials (not committed)
├── .gitignore
├── docs/              # API documentation (53 pages)
└── README.md

Available Tools

1. list_layers

Browse the catalog of 114 data product layers by region, peril, or type.

2. geocode

Convert addresses or coordinates to standardized RMS geocodes with location IDs.

3. lookup_layer

Query a single data layer (hazard, loss cost, risk score, etc.) for a location.

4. composite_lookup

Query multiple layers efficiently in a single request.

5. geocode_reference

Access reference data for countries, admin divisions, postal codes, etc.

Layer Types

• hazard: Raw hazard metrics (requires location only)

• risk_score: Risk scores 0-100 (requires location + building characteristics)

• loss_cost: Expected annual loss (requires location + characteristics + coverage values)

• mmi: Modified Mercalli Intensity (earthquake only, location only)

• depth: Flood depth estimates (location only)

• distance: Distance to features (fault, coast, fire station, etc.)

• special: Custom data products (varies by layer)

Installation

cd C:/Users/khano3/li-rms-mcp
py -m pip install -r requirements.txt

Configuration

Create .env file (already configured):

LI_API_KEY=YOUR_API_KEY_HERE
LI_BASE_URL=https://api-euw1.rms.com

Usage

Start server (stdio)

py server.py

Start server (HTTP)

MCP_TRANSPORT=http PORT=3000 py server.py

Example Workflows

1. Explore available data

list_layers(region="us", peril="eq")
# Returns: us_eq_loss_cost, us_eq_risk_score, us_mmi, us_distance_to_fault

2. Geocode an address

geocode(
    country_code="US",
    street_address="55 Water St",
    city="New York",
    admin1_code="NY",
    postal_code="10041"
)

3. Query single layer

lookup_layer(
    layer="us_eq_risk_score",
    country_code="US",
    latitude=40.7028,
    longitude=-74.0131,
    construction="C1",
    occupancy="COM1"
)

4. Multi-peril analysis

composite_lookup(
    layers=[
        {"name": "us_eq_loss_cost"},
        {"name": "us_fl_loss_cost"},
        {"name": "us_wf_loss_cost"}
    ],
    country_code="US",
    latitude=37.7749,
    longitude=-122.4194,
    construction="W1",
    occupancy="RES1",
    building_value=500000,
    contents_value=250000
)

Data Requirements by Layer Type

Characteristics: construction, occupancy, year_built, num_stories Coverage Values: building_value, contents_value, bi_value

Architecture

• Pattern: edfx-omar MCP pattern (Python + FastMCP + async httpx)

• Transport: stdio (default) or HTTP

• Authentication: API key via Authorization header

• Credentials: .env file with dotenv

• Error handling: httpx automatic retry + raise_for_status()

• Response formatting: JSON with 80k char truncation

Development

Git History

1ec7814 feat: add all 5 tools + main block
c035afe feat: server core — auth, body builder, FastMCP instance
6bceae2 feat: add complete layer catalog
43320c9 chore: project scaffolding

Testing

# Verify layer catalog
py -c "import sys; sys.path.insert(0, '.'); from layers import LAYERS; print(f'{len(LAYERS)} layers')"

# Test tool loading
py -c "import sys; sys.path.insert(0, '.'); from server import list_layers; print(list_layers(region='us')[:200])"

API Documentation

Full API documentation (53 pages) available in docs/ directory.

Next Steps

1. Register in .mcp.json

2. Smoke test with Claude

3. Integration testing with real API calls

4. Performance optimization if needed