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

With no annotations, the description carries the full behavioral burden; it usefully discloses that only status=verified rows are returned and that each row carries a source URL and verification date. It says nothing about authentication, rate limits, pagination, or empty-result behavior, so a mutation-free read tool's operational profile is only partially covered.

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 is front-loaded in the first clause, followed by the verified-rows guarantee and then input examples, so an agent gets the essentials immediately. The bilingual repetition (full Danish text plus an English parenthetical) is mildly redundant but earns its place for cross-language comprehension.

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 single-parameter read-only lookup with no output schema, the description covers what it retrieves, the data-quality filter (verified only), and the key returned fields (source URL, verification date). It does not explain result volume or pagination, but nothing essential for calling it correctly is missing.

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 the baseline is 3, but the description goes beyond the schema's generic 'Emne eller nøgleord at søge efter' by supplying realistic example queries that demonstrate the expected granularity and format (article references, act nicknames, annex references). That added interpretation of the single parameter justifies a step above baseline.

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 gives a specific verb (look up) and a clearly scoped resource (verified EU AI Act/NIS2/Digital Omnibus facts in PowerQuant's sourced fact base), which an agent can readily distinguish from sibling checks such as ai_act_scope_check and art50_transparency_check. It stops short of naming those siblings explicitly, so the differentiation is implicit rather than stated.

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?

Usage is implied through concrete input examples ('art 50', 'omnibus', 'annex iii', 'nis2') and the constraint that only status=verified rows are returned, which tells the agent what this tool is for. There is no explicit when-not-to-use or routing guidance versus the sibling verification/check tools, so the agent must infer the boundary.

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