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calculate_burn_severity

Quantify post-fire burn severity and vegetation destruction using dNBR from pre- and post-fire satellite imagery. Get burned area, severity zones, and damage ratings.

Instructions

Quantify post-fire burn severity and vegetation destruction using the Normalized Burn Ratio Difference (dNBR) from pre-fire and post-fire Sentinel-2 (NIR B08 and SWIR22 B12) observations. Zero-config: Streams public COG tiles without credentials. Classifies according to USGS & EFFIS fire standards.

Args: bbox: Bounding box [min_lon, min_lat, max_lon, max_lat] in WGS84. pre_fire_date_range: Date range prior to the wildfire (e.g. '2023-06-01/2023-06-30'). post_fire_date_range: Date range immediately following the wildfire (e.g. '2023-08-01/2023-08-31'). collection: Satellite collection ('sentinel-2-l2a' or 'landsat-c2-l2'). format: Output format ('summary', 'geojson', or 'csv').

Returns: JSON or formatted string with mean/max dNBR, total burned area (ha), severity zone breakdown, and EFFIS damage rating.

References:

Key, C. H., & Benson, N. C. (2006). USDA Forest Service RMRS-GTR-164-CD, pp. LA 1-55.
Parks, S. A., Dillon, G. K., & Miller, C. (2014). Remote Sensing, 6(3), 1827-1844. DOI: 10.3390/rs6031827

Input Schema

TableJSON Schema

Name	Required	Default
`bbox`	Yes
`format`	No	summary
`collection`	No	sentinel-2-l2a
`pre_fire_date_range`	Yes
`post_fire_date_range`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 23, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations supplied, the description carries the full disclosure burden and adds significant value: it states zero-config access, streams public COG tiles without credentials, names the exact spectral bands, and describes the output. It does not discuss caveats such as cloud cover or data availability, leaving some behavioral detail implicit.

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?

The description is front-loaded with a one-sentence purpose and then organized into Args/Returns sections, making it scannable. The academic references add credibility but are not needed for tool invocation, introducing a minor amount of bloat.

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?

Considering the tool has five parameters, no annotations, and an output schema that already exists, the description covers all call-relevant details: required and optional parameters, output content, standards, and access mode. An agent has everything needed to invoke it correctly.

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 0%, yet the description explains all five parameters with concrete examples: bbox format in WGS84, date range patterns (e.g., '2023-06-01/2023-06-30'), allowed collection values, and allowed format values. It fully compensates for the bare schema and clarifies optional defaults.

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 first sentence uses a specific verb 'Quantify' and names the exact resource ('post-fire burn severity and vegetation destruction'), method ('Normalized Burn Ratio Difference (dNBR)'), and data source ('Sentinel-2 NIR B08 and SWIR22 B12'). This clearly distinguishes the tool from siblings such as detect_active_wildfires or calculate_spectral_index.

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 establishes precise application context: post-fire assessment using pre- and post-fire date ranges and classification to USGS/EFFIS standards. It does not explicitly name alternative tools or exclusion scenarios, so it stops one step short of full guidance.

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