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Glama

power_climate

Retrieve historical daily climate and solar energy data by latitude and longitude, with period averages for temperature, irradiance, and wind.

Instructions

NASA POWER API で任意地点の過去の気候・太陽エネルギー統計を返す(認証不要)。

例:「東京の過去1年間の日射量」「ハワイの気温データ」「太陽光発電の立地評価」
過去の日別データ(通常数日〜数ヶ月前まで)の期間平均を計算して返す。

Args:
    latitude: 緯度(例 東京 35.68)。
    longitude: 経度(例 東京 139.69)。
    start: 開始日 "YYYY-MM-DD"。省略で1年前。
    end: 終了日 "YYYY-MM-DD"。省略で直近データ。
    parameters: カンマ区切りのパラメータ(例 "T2M,ALLSKY_SFC_SW_DWN")。
        省略で主要パラメータ(気温・日射量・風速)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
startNo
latitudeYes
longitudeYes
parametersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.25.2

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral burden. It discloses that no authentication is required and that data is typically several days to months old and returned as a period average, but omits rate limits, error conditions, and response format details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose, then examples, then parameter details. It is appropriately sized and structured for a data-retrieval tool, with each section serving a clear purpose, though the Japanese prose is slightly repetitive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description covers the main invocation needs: purpose, authentication, data latency, averaging behavior, and all parameters. It does not describe the exact return fields or units, but is sufficient for an agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for all five parameters, so the description must compensate. It documents every parameter: latitude and longitude with examples, start/end as YYYY-MM-DD with defaults, and parameters as a comma-separated list with a default set, adding substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (returns) and resource (past climate/solar energy statistics) and specifies the source API (NASA POWER) and no authentication. It distinguishes the tool from space-focused siblings by describing climate/solar data for arbitrary points, but does not explicitly name or contrast with any sibling tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied through examples such as “Tokyo’s solar irradiance over the past year” and “temperature data for Hawaii,” and the data scope is described as period averages of past daily data. There is no explicit guidance on when to use this tool versus alternatives or when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.