get_dataset

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

With no annotations provided, the description carries the full burden of disclosing behavior. It states it returns metadata and field schema, implying a read operation, but does not explicitly declare read-only status, error handling, or any side effects. For a simple fetch tool this is acceptable but not exemplary; more transparency about what happens on invalid IDs would improve it.

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 a single, well-structured sentence that leads with the primary purpose and immediately follows with the key output details. No filler or redundant phrasing; every clause adds value, making it highly efficient.

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 explains the returned content (full metadata, field schema) and why it's needed (query expression writing). It lacks explicit return structure details, but 'full metadata' covers the breadth. Given the tool's simplicity, this is sufficiently complete.

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 complete description of datasetId with an example, so schema coverage is 100%. The tool description does not add additional parameter semantics beyond that, but it does reinforce the purpose by linking the parameter to the returned schema. Baseline 3 is appropriate since the schema already handles the meaning.

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 verb 'Get', the resource 'full metadata for a single dataset', and the identifier 'dataset_id'. It explicitly names the output (field schema with name, type, label, description) and its purpose for writing query expressions, which distinguishes it from siblings like search_datasets or query_records without ambiguity.

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?

It explicitly ties usage to preparing for query_records by mentioning the need for field schema in select/where/order_by expressions. This gives clear context for when to use it, though it doesn't explicitly state when not to use it (e.g., for searching datasets, which would be search_datasets). The guidance is implicit but strong.

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