detect_water_sar

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

With no annotations present, the description carries the behavioral disclosure burden. It clearly states that the tool runs with zero configuration, requires no credentials, classifies pixels below a decibel threshold as water, and returns a JSON string with water percentage and backscatter characteristics. This goes beyond the bare function name and gives useful behavioral context, though it does not mention edge cases or limitations.

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

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

The description is well-structured and front-loaded with the core purpose and zero-config behavior. The Args and Returns sections are compact and directly useful. The references add methodological credibility but are not strictly necessary for tool invocation, so it is concise but not perfectly lean.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three parameters, no annotations, and a schema with zero description coverage, the description covers everything needed to invoke it correctly: required inputs, optional threshold with default, return format, and the underlying method. An agent can confidently call this tool with just bbox and datetime_range based on the provided guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description fully compensates by explaining each parameter: bbox format and coordinate system, datetime_range with an explicit example, and threshold_db with its meaning and default value. This adds essential semantic meaning that the JSON schema alone does not provide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Perform all-weather surface water and flood inundation mapping using Sentinel-1 C-band SAR radar backscatter.' This clearly distinguishes it from sibling tools like detect_dark_vessels or assess_location_hazard by naming the exact task and data source. It is not a tautology and provides enough specificity for an agent to know what the tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when this tool is relevant: all-weather water and flood mapping with SAR. It also highlights a practical condition: zero-config and no API keys needed, which helps an agent decide to use it without credential setup. It does not explicitly name alternatives or exclusions, so it misses the highest bar, but the guidance is clear enough for selection.

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