Describe a dataset

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

Annotations provide readOnlyHint=true, but the description goes further by detailing exactly what metadata is returned (column types/units, time column, resolution, update frequency, actual start/end timestamps). It also notes the dataset-specific coverage window, adding behavioral context beyond the annotation. No contradiction 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no fluff. The first sentence front-loads the purpose and contents; the second provides actionable usage guidance. Every word earns its place, making it both concise and well-structured.

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?

For a one-parameter metadata tool with no output schema, the description fully explains what will be returned and why it's needed before query_dataset. It gives the agent all necessary information to decide when to call it and what to expect, leaving no critical gaps for successful invocation.

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% for the single parameter 'dataset', with a clear explanation ('Exact datasetName from list_datasets, e.g. DayAheadPrices'). The description does not add additional parameter details beyond what the schema already provides, so 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 returns 'Full metadata for one dataset' and enumerates specific fields (columns, types, units, time column, resolution, update frequency, timestamps). This differentiates it from siblings like query_dataset, which is explicitly named, making the purpose unmistakable.

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 instructs 'Call this before query_dataset' and explains why: 'column names cannot be guessed and the coverage window is different for every dataset.' This provides clear when-to-use guidance and even identifies the prerequisite relationship, leaving no ambiguity about when to invoke this tool.

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