acis2llm 0.1.0

Weather and climate data tools powered by NOAA RCC ACIS — MCP server

ACIS2LLM

The professional weather & climate data layer for LLMs.

ACIS2LLM connects your AI agents to the Applied Climate Information System (ACIS), providing high-fidelity historical data and statistical analysis from NOAA Regional Climate Centers—no API keys, no rate limits, just pure climate science.

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🛠 Setup

ACIS2LLM is designed to run via uv for zero-config installation.

1. Claude Code / CLI

claude mcp add ACIS2LLM -- uvx --from acis2llm acis2llm-mcp

2. Gemini CLI

gemini mcp add ACIS2LLM -- uvx --from acis2llm acis2llm-mcp

3. Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "ACIS2LLM": {
      "command": "uvx",
      "args": ["--from", "acis2llm", "acis2llm-mcp"]
    }
  }
}

4. Cursor

Add a new MCP server in Settings > Features > MCP:

• Name: ACIS2LLM
• Type: command
• Command: uvx --from acis2llm acis2llm-mcp

5. Universal MCP (Codex, OpenCode, Pi, etc.)

ACIS2LLM supports any MCP-compliant environment. Generally, you only need to provide the startup command: uvx --from acis2llm acis2llm-mcp

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🌪 Capabilities

ACIS2LLM transforms your LLM into a climate researcher. It doesn't just "get the weather"—it performs statistical analysis over decades of historical records.

1. Geospatial Discovery

• find_best_station: Don't guess IDs. Find the most reliable station near a city or zip code based on historical record length.

2. Statistical Core

• Moments & Distribution: Calculate period_mean, period_median, period_mode, period_standard_deviation, period_variance, period_skewness, and period_kurtosis over any period.
• Percentiles: Determine where a specific value falls in a historical context using period_percentile.

3. Trend & Extreme Analysis

• Threshold Counts: "How many days were above 100°F last summer?" (number_of_days_above).
• Rankings: Automatically find records and extremes with period_rankings.
• Detrending: Remove linear trends from time series data with detrend_data to analyze cyclical patterns.

4. Seasonal & Monthly Aggregates

• seasonal_summary: Analyze meteorological seasons (Winter, Spring, Summer, Fall) across years.
• monthly_totals_by_year: Compare April precipitation across the last 50 years.
• frequency_of_occurrence: Calculate the likelihood of specific events (e.g., "What is % chance of snow in October in Denver?").

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📖 The Climate Cookbook

If you want to know...	Use this tool
"What was the hottest July in NYC history?"	monthly_totals_by_year(station="KNYC", variable="tmax", aggregation="max", month="july")
"Is it likely to freeze in Miami during January?"	frequency_of_occurrence(station="KMIA", variable="tmin", threshold=32, comparison="at_or_below", month="january")
"How does this year's rainfall compare to the 30-year average?"	running_sum + period_mean
"Show me the top 5 snowiest winters in Buffalo."	seasonal_summary(station="KBUF", season="winter", aggregation="sum")

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🗄️ Data & Credits

All data is served in real-time from the Regional Climate Centers (RCCs) via the Applied Climate Information System (ACIS).

• Primary Source: rcc-acis.org
• Engine: Built on xmACIS2Py by Eric J. Drewitz (@edrewitz).

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License: MIT | Author: yenba