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

No annotations are provided, so the description carries the full behavioral burden; it does establish that the tool is non-mutating and informational through the fields it lists. However it says nothing about failure behavior, whether the metadata is cached, or how stale the computed date might be, leaving real gaps for a no-annotation 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?

Two sentences with no filler: the returned fields lead, and the usage rationale follows. Every clause earns its place and nothing is repeated from the title.

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?

With no output schema and no annotations, the description must convey return contents, and it does by enumerating source, computed date, licence and citation plus the dataset's identity. Only the exact output shape (e.g., whether citation is a formatted string or structured fields) is unstated, which is a minor gap.

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 (empty properties object), so per the rubric the baseline is 4 and there is no parameter semantics to document. Nothing in the description misrepresents the absence of inputs.

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?

States a specific resource (the Rechner HQ dataset) and enumerates exactly what it returns: source, computation date, licence and citation. An agent can distinguish it from siblings like dataset_columns, dataset_stats or dataset_row without opening any schema, though it never names those siblings explicitly.

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?

'Read this to attribute a figure correctly' supplies a concrete, actionable trigger for invoking the tool, which is more than most metadata tools offer. It stops short of stating when not to use it or naming alternatives, so it is 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.