# sg-weather-data-mcp

> Use this tool when you need to collect and process historical weather data for various locations, solving problems related to climate analysis, forecasting, and research. It provides an interface for inputting location parameters and outputs formatted weather data, enabling informed decision-making in fields like agriculture, urban planning, and environmental science. Ideal for use in data-driven applications requiring accurate and reliable weather information.

Canonical page: https://skillsregistry.net/skills/vdineshk-sg-weather-data-mcp  
JSON: https://api.skillsregistry.net/v1/skills/vdineshk-sg-weather-data-mcp

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** maps-location
- **Updated:** 2026-09-02

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/vdineshk/sg-weather-data-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": "vdineshk-sg-weather-data-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/vdineshk-sg-weather-data-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/vdineshk-sg-weather-data-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