discover_dataset

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

No annotations are provided, so the description carries the burden. It adds useful behavioral context by explaining that the tool automatically finds the right publication and lists datasets with IDs, and that it replaces a multi-step workflow. It doesn't disclose side effects or limitations, but for a discovery/read tool, this is sufficient context beyond the name.

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?

Two sentences that flow logically: first states the purpose and the workflow it replaces, second gives the recommended next steps. No wasted words, though the second sentence is slightly dense. Still appropriately concise for an orchestration tool.

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-parameter tool with no output schema, the description is complete: it explains what it does, the prerequisite (list_ees_topics), and the follow-up tools (get_dataset_metadata, query_dataset). It also notes that it lists dataset IDs, which is the key information the agent needs to proceed.

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 schema covers the single 'topic' parameter 100%, including examples. The description reinforces this by directing users to list_ees_topics first, adding semantic linkage beyond the schema. This adds value while not repeating verbatim.

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's purpose: 'Discover the publications and datasets available for a specific education data topic.' It also distinguishes itself by referencing the manual 4-step workflow and naming sibling tools like get_dataset_metadata and query_dataset as follow-up steps.

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 usage guidance is provided: 'Use list_ees_topics first to see available topic keys, then use the dataset ID from the results with get_dataset_metadata and query_dataset.' This tells the agent exactly when to use this tool and what to do before/after, effectively replacing the manual workflow.

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