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

With no annotations and no output schema, the description carries the full burden. It does disclose the content returned (source, date, licence, citation), which is useful given the absent output schema, but says nothing about format, permissions, or whether the metadata is static versus versioned.

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, zero filler, with the payload (what it returns) front-loaded before the usage instruction. Every clause 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-argument, read-only metadata lookup this is nearly complete — the description enumerates the returned content, compensating for the missing output schema. It would be fully complete if it hinted at the shape of the response or how stable the citation string is.

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 takes zero parameters, so per the rubric the baseline is 4; there is no parameter semantics for the description to clarify or omit.

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?

Names the specific resource (provenance metadata for the FlightDelayHQ dataset) and enumerates exactly what it returns: source, computation date, licence, citation. An agent can distinguish this from dataset_columns, dataset_stats, or dataset_search without opening any schema.

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?

Provides a clear use case — 'Read this to attribute a figure correctly' — which tells the agent when to reach for it. It does not name alternatives or exclusions (e.g., versus dataset_stats when a citation is needed alongside a figure), so it stops short of full when/when-not routing.

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