describe_dataset

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

No annotations are provided, so the description carries the full burden. It describes the output but does not disclose that the operation is read-only, nor does it clarify behavior when both parameters are provided or error conditions. The existence of an output schema partially mitigates the gap.

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 extremely concise (three sentences), front-loaded with the main purpose, and every sentence adds value—no filler or repetition.

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 tool has two optional parameters, no required ones, and an output schema, the description adequately covers the input options and output summary. Missing details like error handling are minor given the simplicity, and the output schema fills in return structure.

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%, but the description adds examples ('e.g. 'fund.csv'' and 'e.g. 'cfu://funds'') and implies the parameters are alternatives. However, it does not explain when to use one over the other or the exact format expected beyond the examples.

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 uses specific nouns ('Row count, column names, dtypes, and null counts') and implies the verb 'describe'. It clearly distinguishes the resource (dataset) and output, setting it apart from siblings like 'preview_dataset' (shows rows) and 'list_datasets' (lists names).

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 states 'Use when column names are uncertain; skip as a first step if... already lists the columns you need.' This provides clear when-to and when-not-to guidance, though it does not name specific alternative tools.

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