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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds the decoding-use context and the label-or-IRI lookup modes, but does not describe return behavior or edge cases. This is adequate but not rich.

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 one focused sentence and is front-loaded with the key purpose. The trailing 'hint.' appears to be a leftover artifact and adds no value, keeping it from a perfect score.

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 simple lookup tool with fully described parameters and a read-only annotation, the description covers what the tool is for, how it is invoked conceptually, and when it should be used. No critical information 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 coverage is 100%, so parameters are fully documented there. The description adds value by clarifying the intended query style ('by label or IRI') and giving concrete vocabulary examples, going slightly beyond 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?

States a specific verb ('look up'), a specific resource ('Fedlex vocabulary term'), and the method ('by label or IRI'). It also gives concrete vocabulary examples, which clearly separates it from document-reading and search siblings.

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?

Provides a clear use case: decode a coded value from another answer. It does not explicitly name alternatives such as lindas.resolve_label or state when not to use it, but the intended context is clear enough for an agent.

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