get_dataset_info

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

The description accurately lays out the tool's observable behavior: it returns schema metadata, and it cautions that field names are case-sensitive, giving the practical consequence (use the exact 'id'). It does not mention side effects, errors, or authentication requirements—but for a GET-style read-only tool these are seldom necessary. The case-sensitivity disclosure is worth credit because it changes how the agent uses the returned data.

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?

Every portion contributes: the opening line states the operation, the second line supplies usage context with a sequencing cue, the warning adds a necessary behavioral nuance, and the Args block gives the parameter source. No sentence is filler; the length is appropriate for the information density.

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?

With a single required parameter and an output schema present, the description fully covers the needed information: the dataset scope, the source of the identifier, and a critical data-quality principle. The tool is simple enough that no further prerequisites or error-handling notes are needed.

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 provides only the parameter name 'resource_id' with zero description coverage. The description compensates fully by explaining that it is a UUID and pointing the agent to 'search_datasets or the portal' for sourcing it. This eliminates any guesswork about how to obtain or format the value.

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 states the precise operation ('Get the schema of a specific dataset') and enumerates exactly what is returned: title, description, row count, and field names/types. This clearly distinguishes it from the sibling tools (search_datasets searches, query_dataset queries, list_sectors lists sectors) without any 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 gives an explicit sequencing instruction: 'Call this BEFORE query_dataset when you need to know which fields exist or how to spell a filter field.' This tells the agent exactly when to use the tool and what to use with it, and the case-sensitivity warning that has a direct effect on subsequent filter construction. It could mention alternatives more cleanly, but the instruction is refreshingly complete and non-obvious.

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