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

Beyond the read-only/idempotent annotations, it discloses the derivation logic (five yes/no questions with GDPR art. 3 and AI Act art. 2 scope), the shape of the answer (core obligations plus single-law obligations, each with articles and the implementing template), the AI Act establishment/output caveat, and an explicit 'guidance, not legal advice' disclaimer.

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 trigger and scope are front-loaded, but the description is longer than needed: the three law names and their acronyms are repeated across sentences, and "Tells whether the Swiss FADP..., the EU GDPR and the EU AI Act apply" largely restates the opening sentence.

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, the description carries the return-value burden and does so by describing obligations, articles and templates returned per profile. The AI Act nuance is covered; minor gaps remain on whether the response is exhaustive or configurable by lang.

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 description coverage is 100%, so each of the six parameters is already documented in the schema; the description adds only the framing that inputs are treated as scope questions for GDPR art. 3 / AI Act art. 2. It gives no extra interpretation of the individual booleans or the lang enum, so the baseline 3 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 opening sentence names the exact trigger question, the specific laws covered (FADP/LPD/DSG, GDPR, AI Act), and the decision it returns, which clearly separates it from siblings like fadp_obligations or fadp_questions. An agent can route to it 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?

"Use first when someone asks which data protection laws their organisation must follow" is an explicit entry-point condition, reinforced by a concrete example (Swiss company selling online to the EU). It does not name when to prefer fadp_obligations or transfer_check instead, so ordering is implied but alternatives are not spelled out.

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