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Global Temperature Anomaly

climate.indicators.temperature
Read-onlyIdempotent

Retrieve global surface temperature anomalies from NASA GISS. Access monthly readings since 1880, expressed in °C relative to the 1951-1980 baseline. Default returns last 10 years; specify 1-50 years for custom range.

Instructions

Global surface temperature anomaly from NASA GISS — monthly readings since 1880. Values in °C vs 1951-1980 baseline. Returns last 10 years by default (adjustable 1-50). Source: NASA Goddard Institute for Space Studies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearsNoNumber of years of data to return (1-50, default 10). Data is monthly.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
Behavior4/5

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

Annotations already provide readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds valuable context: data source (NASA GISS), baseline period (1951-1980), monthly readings, and that the default is 10 years adjustable. No contradictions with annotations.

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?

The description is three sentences, front-loaded with the core purpose, then units and source. Every sentence adds unique value: what the tool does, how data is measured, default behavior, and attribution. 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 the tool's simplicity (read-only, one optional parameter) and the presence of annotations and output schema, the description is complete. It explains the data nature, units, default, and source. No missing context for an AI agent to select and invoke this tool correctly.

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

Parameters3/5

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

Schema coverage is 100% with a description for the 'years' parameter specifying range and default. The tool description essentially restates the default behavior ('Returns last 10 years by default'), adding minimal extra meaning beyond the schema. Baseline score of 3 is appropriate.

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 it provides global surface temperature anomaly data from NASA GISS, with specific details on units (°C vs baseline), time range (since 1880), and default behavior. It distinguishes from sibling tools like arctic_ice, co2, etc. by focusing on temperature anomaly.

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 implies usage context by stating default return (last 10 years) and adjustability (1-50). However, it does not explicitly mention when not to use this tool versus other climate indicators, or provide alternatives. The guidance is clear but lacks exclusions.

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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