zurich_get_dataset

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, indicating safe, non-destructive behavior. The description adds value by specifying the return format (structured JSON plus markdown) and chaining opportunity, but does not disclose any additional behavioral traits like rate limits or permissions.

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 brief (two sentences plus a returns line), front-loaded with the main purpose, and efficiently conveys key details without extraneous information.

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?

Given the simple single-parameter input, rich annotations, and presence of an output schema, the description sufficiently covers what the tool does, what it returns, and how it connects to other tools, making it complete for its complexity.

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?

The input schema already provides a clear description for the single parameter dataset_id, including examples. The tool description does not add further semantic detail, so it meets the baseline for a well-documented schema parameter.

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 retrieves complete metadata and resources of a dataset, listing specific fields returned (title, description, author, etc.). It distinguishes from siblings like zurich_datastore_query (query data) and zurich_search_datasets (search), making the purpose specific and 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 implies a usage pattern by mentioning the result can be chained with zurich_datastore_query, providing context for when to use this tool. However, it lacks explicit when-not or alternative recommendations, which is a minor gap.

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