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lostnumber07

SHEARLINE

by lostnumber07

get_environment_trend

Retrieve a short forecast series of severe weather parameters (MLCAPE, 0-6 km shear, 0-1 km SRH, supercell composite, significant tornado parameter) from a consistent RAP cycle, with a trajectory interpretation.

Instructions

RAP forecast-environment trend at a CONUS point (the anticipatory view).

Where get_point_environment is "now" (the f00 analysis), this returns a short
forecast series (f00/f01/f03/f06) of the discriminating quantities — MLCAPE,
0-6 km bulk shear, 0-1 km SRH, supercell composite, significant-tornado
parameter — all from one consistent RAP cycle, with an interpretation of the
TRAJECTORY (intensifying / stabilizing / steady). Downloads and decodes four
forecast hours, so the first call can take ~15-20 seconds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYes
lonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Without annotations, the description fully discloses behavior: it downloads/decodes four forecast hours, notes ~15-20 sec initial call, and explains the trajectory interpretation. This exceeds typical transparency.

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 concise with a clear first sentence stating the core purpose. It includes necessary details without excessive verbosity, though slight restructuring could improve readability.

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 the tool's complexity and presence of an output schema, the description adequately covers return values (MLCAPE, shear, SRH, etc.) and latency. Missing error handling or authentication details, but overall sufficient.

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

Parameters2/5

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

Schema coverage is 0% and the description does not elaborate on lat/lon parameters beyond implying a CONUS point. No valid ranges, format, or defaults are given, so the description adds minimal value for parameter usage.

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 returns a RAP forecast-environment trend for a CONUS point, differentiating it from get_point_environment which is 'now'. It lists specific variables and mentions trajectory interpretation, making the purpose unambiguous.

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 contrasts with get_point_environment, indicating when to use this tool for forecast trends rather than current conditions. However, it does not explicitly mention when not to use it or provide alternatives among the nine sibling tools.

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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