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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by specifying the data source (NASA GISTEMP), frequency (annual), and default date range, which goes beyond the annotations.

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 two sentences: the first is information-dense, listing data specifics; the second provides a usage scenario. No wasted words, and the most critical information is front-loaded.

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?

The description lacks details about the output format (e.g., is it a list of objects with year and band? What fields are returned?). Given no output schema, this omission leaves the agent uncertain about what to expect from the tool.

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 100% coverage with descriptions for both parameters. The description adds default values (start_year 1880, end_year current) and the example clarifies usage. This adds meaning beyond the schema alone.

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 clearly states the tool returns NASA GISTEMP annual zonal anomalies for multiple latitude bands, specifying the baseline and units. It distinguishes itself from sibling tools like get_latest_anomaly and get_temperature_anomaly by focusing on regional and polar amplification applications.

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 provides explicit use cases: 'Use for regional climate bets...or bets framed around polar amplification.' It gives context but does not explicitly state when not to use this tool or mention alternatives like get_temperature_anomaly.

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