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 clearly signals a read-only informational operation by enumerating metadata fields and instructing the agent to 'read this.' It does not discuss errors or formatting, but for a zero-parameter provenance lookup the behavior is adequately disclosed.

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, front-loading the data fields before the usage instruction. Every phrase earns its place and there is no fluff or redundancy.

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 tool with no output schema, the description tells the agent both what it returns and why it matters. The agent can select it and know it will get attribution metadata without further documentation.

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?

There are no parameters, which sets a baseline of 4. The description reinforces what the returned content means — source, date, licence, and citation — though there is no parameter-level semantics to clarify.

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 exactly what the tool presents — source, computed date, licence, and citation — for the Cmmsly dataset. The title reinforces that it is about provenance and citation, which is clearly distinct from sibling tools for columns, search, stats, and other data operations.

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 purpose: 'Read this to attribute a figure correctly.' This tells an agent when to use it, though it does not name alternatives or provide an explicit when-not-to-use statement. Still, the context of sibling dataset tools makes the intended use clear.

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