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

Well beyond the readOnlyHint/openWorldHint annotations, it discloses that the lookup is deterministic with no language model, that unrecognised or out-of-scope questions return nothing rather than a constructed answer, that personal-data-looking questions are refused, and that the question is never put into a URL, server log or analytics service. The retention behaviour and the store_for_improvement opt-out are spelled out with a policy reference.

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 core purpose and the two examples are front-loaded, and almost every sentence carries decision-relevant information. It is on the long side, with privacy and retention detail clustered later, but nothing is redundant 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?

With an output schema present, return values need no explanation, and the description still covers everything an agent must know to call it correctly: scope, input sanitisation, refusal behaviour, and data handling. Nothing material is missing for a three-parameter read-only lookup.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds real operational meaning: it tells the caller to rewrite the question in general terms, caps it at 200 characters, forbids names, contact details and identifying numbers, and warns that violations cause outright refusal. The retention opt-out parameter is also explained in prose, not just in the schema.

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 a specific verb (looks the question up in published Praxikon answers) and a tightly bounded resource: short, general questions on the EU AI Act or the GDPR, with two concrete examples. It also explicitly bounds the domain (EU-only, no concrete cases), so an agent can separate it from the coded assessment siblings without opening the 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?

It states when to use it (short general questions, e.g. chatbot disclosure, DPIA triggers) and, crucially, when not to: 'For one concrete system or case, the coded assessment tools give a fuller answer,' which routes to classify_system, check_case or explain_applicability. Out-of-scope law outside the EU is also named as a refusal case.

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