fetch_dataset

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

With no annotations supplied, the description carries the burden and does well: it discloses the overwrite-on-rerun semantics, the exact cache path, and the latency risk for multi-million-row datasets. It does not mention authentication/app-token requirements, rate limits, or what happens on a mid-fetch failure, so it stops short of full behavioral disclosure.

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?

Four sentences, front-loaded with the core action and side effect before the optional filtering and performance caveats. Every sentence carries distinct information, though the parenthetical path and the latency note could be tightened slightly.

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?

An output schema exists, so return shape need not be explained, and the description covers the caching model, overwrite risk, filtering options, and cost profile. Missing pieces are auth expectations and failure/partial-cache behavior, which matter for a network-backed fetch that writes to disk.

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 0%, so the description must compensate. It explains that 'where' is a server-side filter and 'select' is a projection, which is real added meaning over the bare string types, but it gives no SODA/SoQL syntax, no quoting rules, and no examples. dataset_id is left entirely implicit.

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 specific verb and resource (pull every row of a dataset), names the transport (SODA API), and the concrete side effect (cache as Parquet at data/raw/<id>.parquet) plus the downstream reason (SQL cross-referencing). This clearly separates it from describe_dataset (metadata) and query (SQL execution) among the siblings.

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?

Explicit routing guidance: check describe_dataset's cached/fetched_at before re-running, and prefer a $where filter when only a subset is needed. It gives both a prerequisite check and a condition for narrowing scope, which is exactly the when-to-use/when-not information an agent needs.

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