# weatherforensics-mcp

> Use this tool when you need to analyze historical weather data and its localized impact on specific locations and timestamps. It solves problems related to understanding severe weather events and their effects on particular areas, providing comprehensive data on standard conditions and extreme weather. The tool accepts location coordinates and timestamps as input and outputs detailed weather data, making it ideal for applications requiring precise historical weather information.

Canonical page: https://skillsregistry.net/skills/mechatronic-solutions-llc-weatherforensics-mcp  
JSON: https://api.skillsregistry.net/v1/skills/mechatronic-solutions-llc-weatherforensics-mcp

## Description

WeatherForensics is a Data as a Service (DaaS) that provides comprehensive historical weather data, including standard conditions and severe weather events, relative to a specified target location and timestamp. While most services focus on the "what," our proprietary engine calculates the localized impact of historical weather events—ranging from localized tornadoes to regional hurricanes—at a specific coordinate and datetime.

Visit the GitHub repository at https://github.com/markwkiehl/WeatherForensics-MCP to access the sample Python client script (mcp_client_noaa.py). That script will allow you to interact with the WeatherForensics Model Context Protocol (MCP) server.   No registration or API Key required for the "Forever Free" Tier.  

To use the WeatherForensics Model Context Protocol (MCP) directly within Claude Desktop, you must bridge its local standard input/output (stdio) requirement to our remote Streamable HyperText Transfer Protocol (HTTP) server. You do this by pointing Claude Desktop to the included proxy script.

Add the following to your `claude_desktop_config.json` file:

{
  "mcpServers": {
    "weatherforensics": {
      "command": "python",
      "args": [
        "/absolute/path/to/WeatherForensics-MCP/mcp_stdio_proxy.py"
      ],
      "env": {
        "WeatherForensics_API_KEY": "" 
      }
    }
  }
}

- Leave `WeatherForensics_API_KEY` blank to use the Forever Free subscription tier.
- Ensure you replace `/absolute/path/to/` with the actual directory path to the cloned repository on your local machine.

## Trust

- **Trust score (0–1):** 0.75
- **Verification tier:** verified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** version-control
- **Updated:** 2026-09-02

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/mechatronic-solutions-llc/weatherforensics-mcp)
- **Repository:** <https://github.com/markwkiehl/WeatherForensics-MCP>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "mechatronic-solutions-llc-weatherforensics-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/mechatronic-solutions-llc-weatherforensics-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/mechatronic-solutions-llc-weatherforensics-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt