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spectral_savi

Calculate Soil Adjusted Vegetation Index (SAVI) from NIR and Red bands to correct for soil background, ideal for arid and semi-arid environments.

Instructions

Calculate Soil Adjusted Vegetation Index (SAVI) from NIR and Red band reflectance values. SAVI = ((NIR - Red) / (NIR + Red + L)) * (1 + L), where L is the soil brightness correction factor. Default L = 0.5 is suitable for intermediate vegetation cover. L = 0 reduces SAVI to NDVI; L = 1 is for very low vegetation cover. Used in arid/semi-arid regions where soil background significantly affects NDVI.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lNoSoil brightness correction factor L (0–1). Default 0.5 for intermediate vegetation. Use 0 for dense vegetation (equivalent to NDVI), 1 for very sparse vegetation.
nirYesNIR band reflectance value (0–1). Near-Infrared band, typically Sentinel-2 Band 8 or Landsat Band 5.
redYesRed band reflectance value (0–1). Red visible band, typically Sentinel-2 Band 4 or Landsat Band 4.
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses the formula, L parameter behavior (default, range, effects of different values), and mathematical relationship to NDVI. Fully transparent for a calculation tool.

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?

Single paragraph front-loaded with purpose, immediately followed by formula, parameter details, and usage context. Every sentence adds information; no wasted words.

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 output schema, description thoroughly covers inputs, formula, parameter semantics, and use cases. Adequate for a simple mathematical index calculation tool with moderate complexity.

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 100% (baseline 3). Description adds value by explaining the formula, L parameter's soil correction role, and typical values for different vegetation densities. Exceeds baseline by providing context not in 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 clearly states the tool calculates SAVI from NIR and Red bands, including the formula. It distinguishes itself from siblings like NDVI by emphasizing soil adjustment for arid/semi-arid regions.

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?

Explicitly states when to use: 'Used in arid/semi-arid regions where soil background significantly affects NDVI.' Missing explicit when-not or alternatives, but context is clear.

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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