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Satellite Image (NASA)

satellite_image
Read-only

Daily true-color satellite image of any area from NASA GIBS (MODIS Terra/Aqua or VIIRS, about 250 m per pixel, coastlines overlaid) as base64 JPEG. Useful to see clouds, smoke, snow, floods or fires on a given day. Params: place ("Zurich, Switzerland") or bbox [min_lat, min_lon, max_lat, max_lon] or lat + lon + radius_km (5 to 500, default 50), date (YYYY-MM-DD, default yesterday), layer (modis_terra default, modis_aqua, viirs_snpp, viirs_noaa20), width (64 to 1024, default 512), height (default from aspect). Price: $0.013 USDC per call, paid inside this MCP call via x402 (USDC on Base, Solana, Polygon or Arbitrum). x402 MCP clients such as @x402/mcp pay automatically. Free sample: call with the example input {"lat":47.37,"lon":8.54,"radius_km":60,"width":512}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNo
lonNo
bboxNo
dateNo
layerNomodis_terra
placeNoPlace name instead of coordinates, e.g. "Zurich, Switzerland" (GeoNames, cities and towns)
widthNo
heightNo
radius_kmNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / place
      Added value: +{
      +  "description": "Place name instead of coordinates, e.g. \"Zurich, Switzerland\" (GeoNames, cities and towns)",
      +  "type": "string"
      +}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly/openWorld/non-destructive), and the description goes well beyond them by disclosing the cost ($0.013 USDC per call), the payment rail (x402 on Base/Solana/Polygon/Arbitrum), and that compatible clients pay automatically. This is exactly the kind of cost/auth context an agent needs before invoking a paid tool.

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?

Front-loads the purpose, then layers in use cases, params, pricing, and a sample. The param clause is dense, but given the low schema coverage that density is earned rather than wasteful.

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?

For a nine-parameter tool with no output schema and near-zero schema descriptions, the definition covers every parameter, the return format (base64 JPEG), imagery characteristics, cost, and payment mechanics. Nothing an agent needs to call it correctly is missing.

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 coverage is only 11%, but the description compensates thoroughly: it documents the place format, bbox ordering '[min_lat, min_lon, max_lat, max_lon]', the lat+lon+radius_km combination with range (5-500, default 50), date format and default (yesterday), layer options with default, and width/height ranges. All nine parameters are effectively explained.

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?

States a specific verb+resource ('Daily true-color satellite image of any area from NASA GIBS'), names the source and resolution, and names the sensor families (MODIS Terra/Aqua, VIIRS). An agent can immediately distinguish this from siblings like weather, geocode, or satellite_compare.

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?

Gives concrete usage scenarios ('see clouds, smoke, snow, floods or fires on a given day') and points to a free sample input for testing. It lacks explicit when-not guidance or routing to the sibling satellite_compare, so it stops short of full alternative disambiguation.

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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