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

No annotations are provided, so the description carries the full burden. It discloses the exact payload (source, date, licence, citation) for a tool with no output schema, which is the key behavioural fact. It adds nothing about staleness of the computed date or licence caveats, so it is not exhaustive.

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, zero filler, with the returned fields listed up front and the actionable guidance second. Nothing could be removed without losing information.

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 what comes back, and it does list the four returned items. Adequate for a simple metadata lookup, though an agent gets no hint about response shape or whether licensing text is a full deed or a short label.

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 there is nothing for the description to disambiguate. Baseline for a parameter-less tool applies.

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 names the exact resource (the Wärmepumpe Kosten Europa dataset) and enumerates the four things it returns: source, computation date, licence, citation. That clearly separates it from siblings like dataset_columns or dataset_stats. It stops short of a crisp verb, reading as a noun phrase rather than 'Return the...', which keeps it out of the top band.

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' gives a concrete condition for invoking the tool. It does not name alternatives or state when not to use it, but for a zero-parameter metadata lookup the context is unambiguous.

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