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

Annotations already provide readOnlyHint=true and openWorldHint=false, so the safety profile is known. The description adds valuable behavioral context: 'Returns all ~11 datasets by default (no required parameters)' clarifies default behavior, and the list of common datasets with temporal ranges gives the agent expectations. It does not mention pagination or rate limits, but these are not critical given the schema has pagination fields.

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 three sentences: the first states purpose, the second covers default behavior and filtering, and the third lists common datasets and provides usage guidance. Every sentence adds value, and the most critical information (purpose and default behavior) is front-loaded. It is slightly longer than strictly necessary but remains efficient.

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 tool has 9 optional parameters and an output schema, so the description's job is to guide selection and usage. It covers the default behavior, filtering options, and common datasets, and it points to the next tool in the workflow. It does not explicitly explain pagination (limit/offset) or sorting, but those are fully documented in the schema. The description is adequate for an agent to use the tool correctly.

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 description coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining the filtering intent in plain language ('Optionally filter to datasets that contain a specific data type, cover a location or station, or overlap a date range'), which directly maps to datatypeId, locationId, stationId, startDate/endDate. It also gives concrete examples of datatype IDs (e.g., 'TMAX', 'PRCP') and dataset IDs, making the parameters more actionable.

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 states a clear verb+resource: 'List available NOAA CDO datasets with their IDs, names, and temporal coverage.' It distinguishes itself from siblings by explicitly positioning itself as a discovery tool: 'Use this first to discover available datasets before calling noaa_climate_fetch_data.' This makes the tool's role unambiguous.

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 gives explicit guidance on when to use this tool ('Use this first to discover available datasets before calling noaa_climate_fetch_data') and mentions the optional filters that shape usage. It does not explicitly state when NOT to use it or list alternatives beyond fetch_data, but the context is sufficient for an agent to choose it appropriately.

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