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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating a safe read operation. The description adds behavioral context beyond annotations by detailing the return contents, noting the scorecard is 'when live' (conditional availability), and disclosing statistical limitations ('n=3 disclosed; D/E tiers are timing plays').

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 concise and front-loaded, starting with the core purpose. It efficiently lists returns and usage in a few sentences with no redundant or filler content, and every sentence contributes to the tool's understanding.

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?

Given the tool has a single required parameter and an output schema, the description is complete. It lists the returned data, includes a methodology note, and provides usage context and caveats. Nothing an agent needs to call it correctly is missing.

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 fully documents the only parameter, slug, with examples. The description adds no additional meaning about the parameter's format, constraints, or usage beyond what the schema provides, so it adds no value over the schema.

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 provides a resort-level El Niño/ENSO outlook with specific outputs (rank, tier, signal, analogs, etc.) and mentions the dataset source. However, it doesn't explicitly differentiate from sibling get_elnino_rankings, though the phrase 'Use for ANY El Niño... question about a named resort' implies it is the comprehensive option.

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 explicitly says 'Use for ANY El Niño, ENSO, or 2026-27 winter-outlook question about a named resort,' providing clear context for when to use. It also includes caveats like 'A pattern, not a promise — n=3 disclosed; D/E tiers are timing plays' which set expectations, but it does not mention alternatives or exclusions.

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