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sohrab4748

DMAP-AI MCP Server

DMAP-AI MCP Server

Drought Monitoring and Analysis Platform — Model Context Protocol Server

MCP License Live Server Glama Score

DMAP-AI is a remote MCP server by AgriMetSoft LLC that exposes drought monitoring and analysis tools to AI assistants — including Claude, ChatGPT, and any MCP-compatible client. It provides SPI tables, drought severity event detection, wavelet scalogram analysis, and drought periodicity diagnostics over real climate data (NASA POWER and ERA5).


Quick Connect

Claude (claude.ai)

  1. Go to Settings → Connectors → Add custom connector

  2. Name: DMAP-AI

  3. URL: https://droughtanalysis.com/mcp

  4. Click Add, then enable it in your chat via the + button

ChatGPT / Gemini

DMAP-AI may also be available through compatible AI apps or custom assistant workflows. MCP support depends on the client’s current connector features.

Any MCP client (mcp.json / config)

{
  "mcpServers": {
    "dmap-ai": {
      "type": "streamable-http",
      "url": "https://droughtanalysis.com/mcp"
    }
  }
}

Tools

All tools are read-only (readOnlyHint: true, destructiveHint: false). They fetch and compute drought diagnostics from real climate data. No user data is written or stored.

debug_ping

Confirms the server is live and returns the current tool version and available free tools.

Input:  none
Output: server status, tool version, available tools list

run_spi_table

Returns a Standardized Precipitation Index (SPI) table for a given location and time period.

Input:
  latitude        float    e.g. 42.03
  longitude       float    e.g. -93.62
  start_date      string   YYYY-MM-DD  (default: 1981-01-01)
  end_date        string   YYYY-MM-DD  (default: 2024-12-31)
  baseline_start  int      (default: 1981)
  baseline_end    int      (default: 2024)
  spi_scale       int      1–48 months  (default: 12)
  data_source     string   "nasa_power" | "era5"  (default: nasa_power)
  step            string   "yearly" | "monthly"   (default: yearly)
  yearly_method   string   "jan_dec_totals"       (default)
  category_scheme string   "classic_spi" | "usdm_style" | "percentile_band" | "kmeans"
  max_rows        int      (default: 1000)

Output: SPI table with date, precipitation, SPI value, and drought category per row

Example prompt:

"Show me the SPI-12 table for Ames, Iowa from 1981 to 2024."


run_drought_severity_events

Detects and returns drought events (start, end, duration, minimum SPI, magnitude) using a configurable severity threshold.

Input:
  latitude / longitude / start_date / end_date / baseline_start / baseline_end
  spi_scale       int      (default: 12)
  data_source     string   "nasa_power" | "era5"
  step / yearly_method / category_scheme
  severity_threshold  string  SPI threshold, e.g. "-0.99", "-1.5"  (default: -0.99)

Output: drought events table (start date, end date, duration, min SPI, magnitude)

Example prompt:

"Find all drought events in Ames, Iowa from 1981 to 2024 using SPI-12 and a threshold of -1.0."


run_wavelet_scalogram

Runs a continuous wavelet transform and returns a compact time–period power matrix (scalogram), highlighting high-power drought zones.

Input:
  latitude / longitude / start_date / end_date / baseline_start / baseline_end
  spi_scale       int      (default: 12)
  data_source     string   "nasa_power" | "era5"
  step / yearly_method
  max_periods     int      downsampled period axis limit  (default: 24)
  max_times       int      downsampled time axis limit    (default: 60)

Output: compact scalogram matrix, top high-power cells, period and time axes

Example prompt:

"Run a wavelet scalogram for SPI-12 in Ames, Iowa from 1981 to 2024 and show the high-power zones."


run_drought_periodicity_analysis

Computes the global wavelet spectrum and identifies dominant and secondary drought periods (cycles) for a location.

Input:
  latitude / longitude / start_date / end_date / baseline_start / baseline_end
  spi_scale       int      (default: 12)
  data_source     string   "nasa_power" | "era5"
  step / yearly_method

Output: dominant period, top ranked periods by global power, global wavelet spectrum

Example prompt:

"Use DMAP-AI to run global wavelet analysis for SPI-12 in Ames, Iowa from 1981 to 2024. Then explain the dominant period."


Data Sources

Source

Description

Coverage

nasa_power

NASA POWER (default)

Global, ~0.5° grid

era5

ERA5 / Copernicus reanalysis

Global, ~0.25° grid


Supported Category Schemes

Scheme

Description

classic_spi

WMO standard SPI thresholds (default)

usdm_style

US Drought Monitor–style D0–D4 categories

percentile_band

Empirical percentile-based bands

kmeans

AI/ML k-means cluster labels


Notes

  • Free tools: SPI table, drought severity events, wavelet scalogram, periodicity/global wavelet spectrum

  • Not included in the free MCP version: Copula analysis and other advanced licensed features

  • All tools are stateless and idempotent — the same inputs always return the same outputs

  • Data source is NASA POWER by default (no API key required)



Citation

If you use DMAP-AI outputs in research or publications, please cite DMAP-AI and the relevant data source (NASA POWER or ERA5/Copernicus).


© AgriMetSoft LLC. DMAP-AI is a drought monitoring and analysis platform.

A
license - permissive license
-
quality - not tested
C
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

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