What it does
Instead of answering drought questions from general knowledge, an AI assistant connected to this server calls real scientific tools on the DMAP-AI backend. Each tool downloads historical precipitation for the requested coordinates, computes the Standardized Precipitation Index (SPI), and returns structured results the assistant can explain, tabulate, and cite. The same engine powers the DMAP-AI Research Version web app.
Available tools
1. run_spi_table — SPI time series and categories
Returns a historical SPI table (precipitation totals, SPI values, and drought/wetness categories) for a selected point and period. Supports Classic SPI thresholds, USDM-style labels, percentile bands, and k-means category schemes.
| Parameter | Required | Default | Notes |
|---|---|---|---|
latitude, longitude | Yes | — | Point location (decimal degrees) |
data_source | No | nasa_power | or era5 (ERA5-Land/CDS) |
start_date, end_date | No | 1981-01-01 → 2024-12-31 | NASA POWER supports 1981 onward |
baseline_start, baseline_end | No | 1981 → 2024 | Baseline climatology years |
spi_scale | No | 12 | SPI accumulation scale |
step, yearly_method | No | yearly, jan_dec_totals | Temporal aggregation |
category_scheme | No | classic_spi | Category labeling method |
2. run_drought_severity_events — drought event detection
Identifies SPI-threshold drought events and returns the event table: start, end, duration, minimum SPI, and magnitude for each event.
| Parameter | Required | Default | Notes |
|---|---|---|---|
latitude, longitude | Yes | — | Point location |
severity_threshold | No | -0.99 | SPI values at or below this are drought steps |
Plus the same data source, dates, baseline, scale, and aggregation parameters as run_spi_table. | |||
3. run_wavelet_scalogram — time-localized drought variability
Returns a compact wavelet scalogram (power across time and period) showing when SPI variability was strong and at which time scales. Useful for spotting multi-year variability episodes in the record.
| Parameter | Required | Default | Notes |
|---|---|---|---|
latitude, longitude | Yes | — | Point location |
max_periods, max_times | No | 24, 60 | Downsampling caps for the returned matrix |
| Plus the shared data source, dates, baseline, scale, and aggregation parameters. | |||
4. run_drought_periodicity_analysis — dominant drought periodicities
Returns the global wavelet spectrum and ranked periodicity bands across the complete historical record (for example, an approximate 3-, 5-, or 8-year variability band).
| Parameter | Required | Default | Notes |
|---|---|---|---|
latitude, longitude | Yes | — | Point location |
| Plus the shared data source, dates, baseline, scale, and aggregation parameters. | |||
How to connect
Claude (web, desktop, mobile)
- Open Settings → Connectors → Add custom connector.
- Enter the DMAP-AI MCP endpoint:
https://droughtanalysis.com/mcp
- Start a chat and ask, for example: "Run a drought severity analysis for Ames, Iowa, 1981–2024, using NASA POWER."
ChatGPT (Custom GPT)
DMAP-AI is also available inside ChatGPT as a Custom GPT connected to the same MCP tools. See the DMAP-AI ChatGPT page for the direct link and ready-to-use prompts.
Other MCP clients
Any MCP-compatible client can use the server over Streamable HTTP at
https://droughtanalysis.com/mcp. Tools are listed via the standard MCP
tools/list handshake.
Example prompts
- "Use DMAP-AI to generate the SPI table for Ames, Iowa (1981–2024) with NASA POWER and classify each year."
- "Use DMAP-AI to list drought events for 40.7°N, −94.4°E … with duration, minimum SPI, and magnitude, threshold SPI ≤ −0.99."
- "Use DMAP-AI to run a wavelet scalogram for Tucson, Arizona (1981–2024) and describe the high-power zones."
- "Use DMAP-AI to find the dominant drought periodicities for Fresno, California using ERA5-Land."
Scope and limitations
- SPI only. No SPEI, PDSI, soil-moisture, crop-yield, or economic-impact analysis.
- Two datasets. NASA POWER (default) and ERA5-Land/CDS. No other sources.
- Historical and point-based. No forecasts, seasonal outlooks, or climate projections.
- Statistical events. SPI drought events are threshold events, not direct measurements of agricultural or hydrological impacts.
- Wavelet results are descriptive. Periodicities are variability bands in the historical record, not deterministic cycles or predictions.
Frequently asked questions
What is the DMAP-AI MCP server?
A Model Context Protocol server by AgriMetSoft LLC that gives AI assistants direct access to scientific drought-analysis tools: SPI tables, drought events, wavelet scalograms, and periodicity analysis for any point location, using NASA POWER or ERA5-Land historical data.
Which drought indices are supported?
Only SPI at this time. Additional indices (SPEI, PDSI, EDI, and others) are planned for licensed research workflows.
Can it forecast future droughts?
No. All analyses are historical and descriptive.
Is it free?
Yes — the Research Version tools exposed through MCP are free to use.
How should I cite DMAP-AI?
Kolsoumi Ayask, S. (2026). DMAP-AI: Drought Monitoring and Analysis Platform – AI (Version 1.0) [Software]. AgriMetSoft LLC. https://droughtanalysis.com/
Learn more
Knowledge Center · What is SPI? · Wavelet analysis · SPI video tutorial · Open the web app