load_dataset

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

Annotations only declare readOnlyHint and openWorldHint; the description adds genuinely new behavior by explaining the incremental caching policy (reuse fresh archives, build only missing partitions). It does not say how long a download takes, how to detect a partially built dataset, or whether the local cache can be invalidated — but with an output schema present, the remaining omission is acceptable.

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?

Front-loads the action in one sentence, then the sibling relationship, then the call-order advice and a pointer to a companion tool. No filler sentences; every clause carries either purpose, contrast, or sequencing.

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?

With annotations, a full-coverage schema, and an output schema, the description covers the workflow placement and non-destructive caching contract an agent needs. Minor gap: it does not suggest cache_status for verifying what is already loaded, which would complete the loop for repeated calls.

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 table, period, and dataset semantics are already fully documented in the schema, including the '*' wildcard, comma-separated periods, and dataset id examples. The description adds nothing specific about parameters beyond mentioning 'periods' incidentally, so baseline 3 applies.

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?

States a concrete verb and resource ('Download and parse a dataset/period into the local cache') and immediately positions it against a named sibling ('non-destructive counterpart of refresh_cache'). An agent can distinguish it from refresh_cache, cache_status, and run_query without opening any schema.

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?

Gives an explicit sequencing rule ('Call it before run_query so the SQL layer has the tables it needs') and routes to a related tool for follow-up ('use describe_dataset(detail=True) for column meanings'). It also names the alternative (refresh_cache) and the condition that selects this one: archives still fresh, only missing partitions built.

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