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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly lists what the tool returns (source, computed date, licence, citation) and signals a read-only intent via 'Read this.' This is sufficient for a simple provenance tool, though it does not describe exact output formatting.

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 carry all essential information: what the tool provides and when to use it. The key content list is front-loaded and there is no filler.

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, no-output-schema provenance tool, the description is fully adequate. It names the dataset, the exact provenance fields available, and the intended use case, leaving no critical gap for an agent to call it correctly.

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 there is no parameter semantics burden on the description. The baseline for no parameters is 4, and the description appropriately focuses on the tool's purpose rather than parameter explanation.

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 states a specific resource ('the Fitouto dataset') and the content it provides: source, computed date, licence, and citation. It is immediately distinguishable from sibling tools like dataset_columns and dataset_row, which concern data structure/content, not provenance.

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 gives an explicit use case: 'Read this to attribute a figure correctly.' This tells the agent when to invoke the tool, though it does not mention alternatives or conditions for not using it. Clear context without exclusions.

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