Skip to main content
Glama
IBM
by IBM

stac_download_rgb

Download a true-color RGB composite from a STAC scene for visual satellite imagery. Automatically selects red, green, blue bands, with options for cropping, cloud masking, and output format.

Instructions

Download a true-color RGB composite from a scene.

Convenience wrapper around stac_download_bands that automatically selects red, green, blue bands. This is the simplest way to get a visual satellite image.

Args: scene_id: Scene identifier from a previous stac_search call bbox: Optional crop bbox in EPSG:4326 [west, south, east, north]. Strongly recommended to avoid downloading full tiles output_format: "geotiff" (default, lossless) or "png" (8-bit lossy, suitable for inline display by LLMs) cloud_mask: Apply SCL-based cloud masking (Sentinel-2 only) output_mode: Response format - "json" (default) or "text"

Returns: JSON with artifact_ref for the RGB composite

Tips for LLMs: - Use output_format="png" when the user wants to see the image — PNG can be rendered inline - GeoTIFF preserves full 16-bit precision but cannot be displayed inline - For false-color composites (e.g., NIR/Red/Green), use stac_download_composite instead - Only works for optical collections (Sentinel-2, Landsat) that have red, green, blue bands

Example: rgb = await stac_download_rgb(scene_id="S2B_...", output_format="png")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bboxNo
scene_idYes
cloud_maskNo
output_modeNojson
output_formatNogeotiff
Behavior4/5

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

With no annotations, the description carries full burden. It states it is a convenience wrapper, automatically selects bands, applies cloud masking optionally, and returns JSON with artifact_ref. It does not mention side effects (likely none), but is transparent about constraints (optical only). Minor gap: no explicit mention of idempotency.

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?

Well-structured with summary, args, returns, and tips. Every sentence serves a purpose. The 'Tips for LLMs' is slightly long but adds practical value. Could be slightly more concise, but overall effective.

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, description explains return format (JSON with artifact_ref). It covers parameters, usage context, and tips. Missing explicit error handling or prerequisites beyond optical collections, but still fairly complete for a 5-parameter tool.

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 coverage is 0%, so description must compensate. It describes scene_id, bbox (strongly recommended), output_format (geotiff vs png with use cases), cloud_mask (SCL-based, Sentinel-2 only), and output_mode (json or text). It adds value beyond schema, e.g., why bbox is important and when to use PNG. However, output_mode's options are not fully detailed.

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 it downloads a true-color RGB composite from a scene, distinguishing it from stac_download_bands (convenience wrapper) and stac_download_composite (false-color). The verb 'download' and resource 'RGB composite' are specific and unambiguous.

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?

Explicitly provides when to use (RGB visual) and when not (false-color -> use stac_download_composite). Includes tips for LLMs on output_format choices, and states it only works for optical collections. This is comprehensive guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/IBM/chuk-mcp-stac'

If you have feedback or need assistance with the MCP directory API, please join our Discord server