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

No annotations are provided, so the description carries the behavioral burden. It discloses what is returned (including the unusual "provenance banner"), which implies a safe read-only operation, but says nothing about cost, permissions, or pagination. Adequate for a zero-parameter read but not rich.

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?

A single front-loaded sentence that enumerates the return payload, followed by a short directive on when to call it. No filler, nothing to trim.

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 no output schema, the description usefully enumerates the returned fields, compensating for the missing return contract. It is nearly complete for a zero-param read tool, though the phrase "provenance banner" is unexplained and the output format is not characterized.

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 takes no parameters, so there is nothing for the description to disambiguate and the baseline is 4. The description correctly adds no parameter noise.

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?

States specifically what it returns: columns, which are numeric, row count, and provenance banner of a named dataset. That verb+resource framing lets an agent know exactly what it gets. It doesn't explicitly contrast itself with dataset_provenance or dataset_stats, which invites some overlap confusion.

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?

"Call this first to learn the schema" gives a clear ordering directive that tells the agent when to reach for this tool relative to the other dataset_* tools. It stops short of naming alternatives or exclusions, so it is clear context rather than full routing guidance.

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