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space-mcp-server

space-mcp-server

by AiAgentKarl
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

get_earth_imagery

Fetch real-time Earth imagery from NASA's DSCOVR satellite, showing full-disk views of clouds, continents, and oceans. Specify a date or limit the number of images.

Instructions

Echte Fotos der Erde von NASAs DSCOVR-Satellit (EPIC-Kamera).

Liefert Bilder der ganzen Erde aus 1,5 Millionen km Entfernung am Lagrange-Punkt L1. Zeigt Wolken, Kontinente und Ozeane.

Args: date: Bestimmtes Datum YYYY-MM-DD (optional, Standard: neueste) limit: Maximale Anzahl Bilder (Standard: 5, Maximum: 20)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_earth_imagery"
  }
}

Input Schema

NameRequiredDescriptionDefault
dateNo
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses the satellite, distance, and content. It does not mention rate limits, authentication, or potential errors, but for a simple image retrieval tool, the level is adequate.

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?

The description is extremely concise with two short paragraphs plus an Args section. Every sentence adds value, and the essential information is front-loaded. No unnecessary words.

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, the description explains the tool returns images but does not specify the response format (e.g., URLs vs binary). It fully documents input parameters but lacks output structure details, which is a minor gap.

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?

Despite 0% schema coverage, the description adds full meaning for both parameters: date (format YYYY-MM-DD, optional, default latest) and limit (type integer, default 5, max 20). This compensates completely for the lack of schema descriptions.

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 retrieves real Earth photos from NASA's DSCOVR satellite (EPIC camera), specifying the satellite, location (L1), and content (clouds, continents, oceans). This distinguishes it from sibling tools like get_exoplanets or get_mars_rover_photos.

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 provides clear context about the tool's unique source and output, implying when to use it (for Earth imagery from DSCOVR). It does not explicitly state when not to use it or mention alternatives, but the specificity makes it sufficient.

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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