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

With no annotations provided, the description carries the behavioral burden. It discloses what the agent will receive and frames the call as a schema-learning step, implying a safe read-only metadata operation. It stops short of explicitly stating that there are no side effects or permissions required, but such concerns are minimal for a parameterless metadata tool.

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 short sentences with no wasted words: the first lists the output contents, and the second gives the usage directive. Nothing is redundant with the title, and the most actionable instruction is front-loaded.

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 parameterless introspection tool, the description covers the core return contents and the intended first-call behavior. The exact return shape is not specified, but the low complexity and the described output make the tool usable even without an output schema.

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 accepts zero parameters, so the description does not need to explain any argument semantics. The baseline 4 applies, and the description adds no unnecessary parameter-related detail.

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 identifies what the tool returns—column names, numeric flags, row count, and provenance banner—so the agent knows what to expect. It is positioned as the schema-discovery entry point among siblings, though it lacks a strong imperative verb like 'list' or 'get' and does not explicitly contrast with dataset_provenance despite overlapping banner content.

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 description explicitly tells the agent to 'Call this first to learn the schema,' providing a clear when-to-use signal. It does not mention when to use alternatives, but for a first-step introspection tool the intended placement is fairly obvious.

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