list_dataset_fields

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds the return shape, which matters since there is no output schema, though it says nothing about pagination or result size.

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?

Three tight parts — purpose, invocation guidance, then the ID reference list — front-loaded with the action and nothing redundant.

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 single-enum-parameter tool with no output schema, the description supplies both the parameter meaning and the return shape. An agent has everything needed to call it 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 coverage is 0% and the enum carries no descriptions, yet the description maps every allowed dataset_id value to a human-readable dataset name. This is exactly the meaning the schema lacks, so it fully compensates.

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 ('List') and resource ('filterable fields of a searchable dataset'), and enumerates the returned shape (field name, type, description). It is clearly distinguishable from search_dataset and the other scraper 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?

Explicitly prescribes sequencing: 'Call this before search_dataset to learn which field names and types you can filter on.' It names the sibling it feeds and the reason to invoke it, leaving nothing to inference.

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