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

With no annotations, the description carries the burden of behavioral disclosure. It clearly states the tool returns provenance metadata (source, date, licence, citation) and is a read-only informational tool. However, it doesn't disclose whether the data is static or computed on demand, whether it returns a single object or a list, or any formatting details. The description is honest but minimal.

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 sentences, no wasted words. The first sentence lists the exact contents, and the second gives the use case. Every word earns its place.

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 informational tool, the description is nearly complete. It tells the agent what data is returned and when to use it. The only gap is the lack of an explicit statement about the return format (e.g., plain text vs structured object), but with no output schema and no parameters, the description covers the essential context.

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 the schema provides no parameter semantics at all. The description compensates by explaining what the tool returns and why an agent would call it. With 0 params, baseline is 4, and the description meets that by giving context about the dataset and its purpose.

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 a specific verb ('Read this to attribute a figure correctly') and a clear resource: provenance metadata for the Corp Tax Calculator dataset. It names the exact contents (source, date computed, licence, citation), which distinguishes it from sibling tools like dataset_columns or dataset_stats. The title reinforces the purpose without being a tautology.

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 it: when needing to attribute a figure correctly. It implies this is the provenance/citation tool among the dataset_* siblings, though it doesn't explicitly name alternatives or exclusions. The context is clear enough for an agent to select it over dataset_columns or dataset_stats.

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