NCEI Daily Climate Data

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds valuable behavioral details beyond annotations, such as units (tenths of °C and mm) and the source dataset, which help agents understand output format. No contradictions.

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 concise sentences, front-loaded with the main function. Every sentence adds value without redundancy. It efficiently covers purpose, source, units, and constraints.

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 existence of an output schema, the description adequately covers the tool's functionality, constraints, and source. It mentions the prerequisite station tool and date range limit. Could be slightly more complete with explicit mention of rate limits or pagination, but overall sufficient.

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?

Schema coverage is 100% with parameter descriptions. The description adds contextual value (time span, units) but does not provide significant extra meaning for each parameter beyond what the schema offers. Baseline 3 is appropriate.

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 retrieves historical daily weather observations from NOAA NCEI, specifying data types (temperature, precipitation, etc.), time span (260+ years), units (tenths of °C and mm), and source (GHCND dataset). It distinguishes itself from sibling tools like ncei.climate.stations by focusing on data retrieval.

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 clear usage context: it mentions the prerequisite station ID from the ncei.stations tool, date range constraints (max 1 year), and optional data types. However, it lacks explicit when-not-to-use guidance or comparisons to other weather tools.

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