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

With no annotations provided, the description carries the full behavioral disclosure burden. It clearly reveals what the call returns: columns, numeric flags, row count, and a provenance banner, and implies a read-only schema-inspection operation. It could mention side effects or the absence of modification explicitly, but the content disclosure is solid.

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 two short sentences that front-load the returned information and end with an actionable directive. Every word earns its place, and the structure makes the tool's role immediately clear.

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 zero-parameter introspection tool with no output schema, this description is complete enough. It states what information will be received, identifies the target dataset, and tells the agent exactly when to invoke it, which is sufficient for correct usage.

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 tool has zero parameters and the input schema is empty, so there is no parameter meaning to add. Per the baseline guidance for zero-parameter tools, a score of 4 is appropriate because there is nothing the description needs to compensate for.

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 returns the columns, numeric-type indicators, row count, and provenance banner of the Jobcardo dataset, and explicitly frames it as the first call to learn the schema. The phrase 'Call this first' also positions it apart from sibling tools like dataset_compare, dataset_search, and dataset_stats.

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 instruction 'Call this first to learn the schema' gives clear usage context: this is the entry point for understanding the dataset before using other tools. It does not explicitly name alternatives or state when not to use it, but the 'first' guidance is a strong and unambiguous usage signal.

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