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 transparently lists what information the tool returns, and the 'Read this' phrasing implies a non-mutating informational operation. It does not discuss side effects, but none are plausible for a provenance retrieval tool.

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 a single compact sentence that immediately names what the tool returns, followed by a short actionable purpose. Every word earns its place, and the title reinforces the core idea without 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 zero-parameter, metadata-retrieval tool with no output schema, the description is complete: it names the dataset, lists the key provenance fields, and explains why an agent would use it. No critical information needed for correct invocation is missing.

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 and the schema already documents this fully with 100% coverage. Description adds meaningful context about what the returned provenance information contains, which is appropriate for a parameterless tool.

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 that the tool provides provenance metadata for the Carbikly dataset: source, computed date, licence, and citation. This is distinct from the sibling data-manipulation tools, though it does not explicitly name or contrast them.

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 instruction 'Read this to attribute a figure correctly' gives a clear, concrete use case for when to invoke the tool. It does not state exclusions or alternatives, but for a metadata/citation tool this is a sufficiently clear context.

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