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

Goes far beyond the sparse annotations. It discloses that English terms and numbers pass through unchanged, explains the verification semantics ('verified: true' vs fallback to unchanged input), states the 10,000 character limit, and notes the monthly quota. This is rich, non-obvious behavioral context that an agent needs to interpret results correctly.

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 dense but every sentence earns its place: purpose, pass-through behavior, verification semantics, limits, and quota. It is front-loaded with the core action and use cases, then moves to details an agent needs for correct invocation and result interpretation.

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 tool with an output schema, the description covers input constraints, behavioral edge cases, output verification, and usage context. Nothing essential is missing for correct selection and invocation.

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%, and the schema description already documents the max length and English/number pass-through behavior. The tool description repeats this but adds no meaningful new parameter semantics beyond what the schema provides, so the baseline of 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?

States a specific verb and resource: 'Add full nikud (vowel points) to Hebrew text'. The purpose is elaborated with concrete use cases (learners, vocalized display, text-to-speech) and the motivation (unvocalized Hebrew is ambiguous), making it clear what the tool does and why it exists. The sibling strip_nikud is implicitly distinct as the inverse operation.

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 explicit context for when to use the tool: for learners, vocalized display, and text-to-speech, or when unvocalized Hebrew is ambiguous. It does not explicitly name alternatives or exclusion cases, but the use-case framing is sufficiently clear for an agent to select it appropriately.

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