eo-mcp

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detect_active_wildfires

Detect active wildfires and thermal anomalies from NASA FIRMS data, providing fire perimeters, radiative power, and danger classification without requiring API keys.

Instructions

Detect active wildfires and thermal anomalies from open NASA FIRMS feeds (VIIRS / MODIS) and delineate clustered fire perimeters with Fire Radiative Power (MW) and fire danger classification. Zero-config: Uses open NASA FIRMS feeds. No credentials or API keys required.

Args: bbox: Bounding box [min_lon, min_lat, max_lon, max_lat] in WGS84. days: Observation lookback in days (1 to 10). Default is 2. source: Sensor product ('VIIRS_NOAA20_NRT', 'VIIRS_SNPP_NRT', or 'MODIS_NRT'). format: Output format ('summary', 'geojson', or 'csv').

Returns: Hotspot locations, Fire Radiative Power (MW), brightness temperature (K), clustered fire perimeters, and EFFIS fire danger rating.

References:

Schroeder, W., et al. (2014). Remote Sensing of Environment, 143, 85-96. DOI: 10.1016/j.rse.2013.12.008
Giglio, L., et al. (2016). Remote Sensing of Environment, 178, 31-41. DOI: 10.1016/j.rse.2016.02.054
Wooster, M. J. (2003). Remote Sensing of Environment, 86(1), 83-107. DOI: 10.1016/S0034-4257(03)00070-1

Input Schema

TableJSON Schema

Name	Required	Default
`bbox`	Yes
`days`	No
`format`	No	summary
`source`	No	VIIRS_NOAA20_NRT

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 20, 2026

TDQS

A4.1/5.0

Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It discloses the absence of required credentials and the open NASA FIRMS data source, which is useful. However, it does not mention side effects, rate limits, network dependence, or whether the operation is strictly read-only, though 'detect' implies it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The operational part is structured well with Args and Returns sections, and the main purpose is front-loaded. However, three full academic citations with DOIs add substantial length and do not directly help an agent select or invoke the tool, so the description is not optimally concise.

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 four parameters, no schema descriptions, and no annotations, the description covers all required invocation details: parameter semantics, defaults, no-auth behavior, and expected returns. The output schema exists and covers return structure, so the missing details like output format specifics are not a serious 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?

Schema description coverage is 0%, but the Args block fully compensates by explaining every parameter: bbox order and WGS84 coordinate system, days range (1 to 10) and default, allowed source products, and allowed output formats. This is exactly the kind of semantic detail an agent needs beyond the bare 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 opens with a specific verb-resource pair: 'Detect active wildfires and thermal anomalies from open NASA FIRMS feeds (VIIRS / MODIS)', and further narrows scope by mentioning clustered perimeters, Fire Radiative Power, and fire danger classification. This clearly distinguishes it from fire-related siblings like calculate_burn_severity.

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 gives clear usage context: it is for active wildfire detection from NASA FIRMS and emphasizes 'Zero-config' and 'No credentials or API keys required.' It does not explicitly name alternatives or exclusions, but the context is strong enough for an agent to select it over non-fire detection tools.

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