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space-finder-mcp

by loosephoto
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astronomy_weather

Find the best nighttime windows for astronomical observation at any location by analyzing cloud cover and visibility, with AI advice on chances and cautions.

Instructions

天体観測に最適な時間帯を予報する（Open-Meteo, 認証不要・全世界対応）。

指定した地点の今後数日間で「夜間かつ雲が少なく・視程が良い」時間帯を抽出し、
観測のチャンスと注意点を AI からのアドバイス形式で返す。

例:「東京で今夜天体観測できる?」「シドニーの晴れ予報」「富士山頂の雲量」
場所は緯度経度(latitude/longitude)または地名(place)で指定。

Args:
    latitude: 緯度（例 東京 35.68）。place 指定時は省略可。
    longitude: 経度（例 東京 139.69）。
    place: 地名（緯度経度より優先）。Open-Meteo のジオコーディングで解決。
    days: 予報日数（1〜7、既定 3）。
    max_cloud: 「観測可」とみなす雲量の上限%（既定 40）。
    include_rain: 日本国内の地点に限り、気象庁の雨雲・降水画像
        （解析雨量・降水短時間予報のパネル＋ナウキャスト＝雨雲の動き）を
        添付する（既定 True。取得失敗時は添付なしで続行）。
        ⚠️ structuredContent.figure.notes は要約せずそのまま引用すること。
        （添付した雨雲・降水画像は content に「🖼️ [◯◯を開く](URL)」のリンク付きで出します。
        インライン画像を描画しないハーネスでは、回答にこのリンクをそのまま提示してください。）

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
placeNo
latitudeNo
longitudeNo
max_cloudNo
include_rainNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.30.1
    • removedInput schema / properties / include_chart
      Removed value: -{
-  "default": true,
-  "title": "Include Chart",
-  "type": "boolean"
-}
    • addedInput schema / properties / include_rain
      Added value: +{
+  "default": true,
+  "title": "Include Rain",
+  "type": "boolean"
+}
  2. Changed1 schema field changedv0.29.1
    • addedInput schema / properties / include_chart
      Added value: +{
+  "default": true,
+  "title": "Include Chart",
+  "type": "boolean"
+}
  3. Addedv0.25.2

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses no authentication needed, global coverage, advice-style output, the rain-imagery attachment behavior, the failure fallback, and the instruction to preserve structuredContent.figure.notes and image links. It does not mention rate limits or explicitly state read-only behavior, but the forecast nature makes this mostly implicit.

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 the core purpose, followed by examples and then a structured Args list. It is longer than minimal, but the extra detail, especially the rain-image handling notes, is operationally useful rather than filler. The organization keeps it readable.

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?

For a tool with six parameters and no output schema, the description covers location specification, parameter semantics, output behavior, and edge cases such as failed image retrieval. It does not explain the exact output format beyond 'AI advice' and optional image links, and it avoids discussion of timezone handling or validation errors, but the agent has enough to invoke it correctly.

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

Parameters5/5

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

The schema provides no property descriptions, but the description compensates thoroughly by documenting all six parameters: latitude, longitude, place precedence over coordinates, days range with default, max_cloud threshold meaning, and include_rain behavior plus fallback. This adds substantial meaning beyond the bare schema.

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

Purpose5/5

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

The description states a clear purpose: forecasting the best time windows for astronomical observation based on nighttime, low cloud, and good visibility. It names the data source (Open-Meteo), authentication requirement, and global scope, and the examples (e.g., 'Can I stargaze in Tokyo tonight?') make the resource and intent unambiguous.

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

Usage Guidelines4/5

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

The description gives concrete usage context through examples, explains how to specify location via coordinates or place name, and documents parameter ranges and precedence rules. It does not explicitly compare this tool to any sibling tool or state when not to use it, so there is a slight gap in exclusion guidance.

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

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