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

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly states that this is a read operation ('Read this') and enumerates the informational fields returned. However, it does not describe any output format, error behavior, or whether the data is static or fetched dynamically, though for a zero-parameter provenance tool this is a minor gap.

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 sentences with no redundancy. The first sentence packs the core content categories, and the second sentence states the practical use case. It is appropriately front-loaded and easy to scan.

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 simple zero-parameter metadata tool, the description is largely complete: it names the dataset, the fields returned, and the reason to call it. The lack of an output schema is partially mitigated by the explicit list of content categories, though the exact structure of the return value is left unspecified.

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, so parameter semantics are not applicable. Per the calibration baseline, a no-parameter tool receives a 4; the description adds contextual value by explaining what the returned provenance information will contain, which is more than is required.

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 names the specific resource (CoilDesk dataset provenance) and the exact content categories: source, computation date, licence, and citation. It also gives the intended use ('to attribute a figure correctly'), which clearly distinguishes it from sibling tools like dataset_stats or dataset_row.

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 when to use this tool: when attribution for a figure is needed. It does not explicitly name sibling alternatives or give when-not-to-use conditions, but the attribution context is clear enough for selection among the listed siblings.

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