unicode_normalize

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

The description explains the normalization process, the meaning of `changed`, and the return object. It includes an example demonstrating the transformation. With no annotations, the description carries full burden and does so adequately, though edge cases or errors are not mentioned.

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 concise and well-structured, with two paragraphs and an example. Every sentence adds value and there is no redundancy.

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?

Given the lack of output schema, the description explains the return object adequately. It covers normalization forms and provides an example. Missing error handling or invalid input cases, but for a simple tool this is acceptable.

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 baseline is 3. The description repeats the schema's parameter descriptions but adds a helpful example. The additional value is moderate.

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 clearly states the tool normalizes text to a Unicode normalization form, lists the four forms, and distinguishes between canonical and compatibility variants. The example clarifies the behavior, making the purpose unambiguous.

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 what the tool does, but does not explicitly state when to use it vs. alternatives. However, the sibling tools are sufficiently different (encoding, hashing), so the lack of explicit exclusion is not critical.

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