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IBM

chuk-mcp-celestial

by IBM

get_sky

Get a complete sky summary showing visible planets with positions, moon phase, and darkness conditions for astronomical observation at your location and time.

Instructions

Get a complete sky summary — all planets, moon phase, and darkness — in one call.

Returns which planets are visible, their positions and brightness, the current moon phase, and whether the sky is dark enough for observation. This is the recommended tool for "what's in the sky tonight?" questions.

Args: date: Date in YYYY-MM-DD format (e.g., "2026-2-10") time: Time in HH:MM format, 24-hour (e.g., "21:00"). UTC unless timezone specified. latitude: Observer's latitude in decimal degrees (-90 to 90) longitude: Observer's longitude in decimal degrees (-180 to 180) timezone: Timezone offset from UTC in hours (e.g., 0 for GMT, -5 for EST, 1 for CET). When provided, the time parameter is interpreted as local time.

Returns: SkyResponse: GeoJSON Feature containing: - properties.data.visible_planets: Planets above horizon and not lost in sunlight, sorted brightest first. Each has altitude, azimuth, direction, magnitude, constellation, and visibility status. - properties.data.all_planets: All 8 planets regardless of visibility - properties.data.moon: Current phase and illumination percentage - properties.data.is_dark: True if sun is below -6 degrees (civil twilight) - properties.data.summary: One-line text summary for quick display

Tips for LLMs: - Use this instead of calling get_planet_position 8 times - The summary field gives a quick human-readable answer - visible_planets are sorted brightest first (lowest magnitude) - direction field gives compass bearing: "S" = look south, "NE" = northeast - is_dark=False means it's daytime or twilight — planets may not be visible even if above horizon - Combine with weather forecast to check if skies are clear enough to observe

Example: sky = await get_sky( date="2026-2-10", time="21:00", latitude=51.99, longitude=0.84, timezone=0 ) for p in sky.properties.data.visible_planets: print(f"{p.planet}: {p.direction}, magnitude {p.magnitude}, in {p.constellation}") print(sky.properties.data.summary)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYes
timeYes
latitudeYes
longitudeYes
timezoneNo
Behavior4/5

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

With no annotations, the description carries full burden and provides substantial behavioral context: it explains what data is returned (planets, moon phase, darkness indicator), sorting order ('brightest first'), interpretation of fields ('is_dark=False means daytime or twilight'), and practical implications for observation. It doesn't cover rate limits or authentication needs, but offers rich operational guidance.

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

Conciseness5/5

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

Well-structured and front-loaded: purpose first, then returns, args, tips, and example. Every sentence adds value—no fluff. The tips section efficiently addresses common LLM usage scenarios without redundancy.

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

Completeness5/5

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

Given no annotations and no output schema, the description provides complete context: it explains the tool's behavior, parameters, return structure (including nested GeoJSON details), and practical usage tips. It covers all necessary aspects for effective tool invocation without relying on structured fields.

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?

Schema description coverage is 0%, but the description fully compensates by detailing all 5 parameters with clear semantics, formats, and usage rules (e.g., 'UTC unless timezone specified', ranges for latitude/longitude, timezone offset examples). It adds meaning beyond the bare schema, making parameters fully understandable.

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 clearly states the tool's purpose with specific verbs ('Get a complete sky summary') and resources ('all planets, moon phase, and darkness'), distinguishing it from sibling tools like get_planet_position by emphasizing it's a comprehensive alternative to multiple individual calls.

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

Usage Guidelines5/5

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

Explicit guidance is provided: 'This is the recommended tool for "what's in the sky tonight?" questions' and 'Use this instead of calling get_planet_position 8 times.' It also mentions when to combine with weather forecasts, giving clear context for usage versus alternatives.

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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