record_literacy

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

No annotations are provided, so the description must carry the transparency burden. It does so by stating that 'The article asks for measures, not a level reached by any individual, so no score is recorded', which clarifies an important behavioral trait (it records measures, not scores). It also lists allowed values for key parameters, giving insight into expected inputs. It omits details like side effects, reversibility, or success/failure behavior, but for a record tool this is reasonably transparent.

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 description is a single, compact paragraph. It front-loads the core purpose and then lists enumerations efficiently. It is not overly verbose, though the density of information (multiple parameter lists) makes it a bit packed. It could be slightly more scannable, but overall it is concise and structured.

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 10 parameters, 4 required, and no output schema, the description needs to cover the critical aspects. It covers the domain and highlights key parameter meanings, but many parameters remain unexplained. There is no mention of uniqueness constraints, idempotency, audit behavior, or how it interacts with other records. The absence of side-effect disclosure and incomplete parameter documentation makes this insufficient for a tool of this complexity.

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 schema has 0% description coverage, so the description must compensate. It explains `measure`, `audience`, and `considered` with their allowed values, which is helpful. However, it does not explain the remaining 7 parameters (e.g., `actor`, `ts`, `system`, `context`, `refers_to`, `description`, `persons_affected`). These are left to their names only, which may be ambiguous. The partial coverage earns a 3.

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 begins with 'Record one AI-literacy measure (EU AI Act Art. 4)', which is a specific verb and resource. It also enumerates the allowed values for `measure` and `audience`, making it easy to distinguish this tool from siblings like `record_oversight` or `record_lifecycle`. The explicit reference to the EU AI Act Art. 4 further sharpens the purpose.

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?

The description provides clear context on when this tool is meant to be used (for AI-literacy measures per Art. 4) and clarifies that it does not record a per-individual level. However, it does not explicitly name any sibling tools or state when NOT to use this tool in favor of alternatives, leaving some ambiguity. The domain specificity is strong, but explicit exclusions are missing.

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