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

With no annotations provided, the description is the only source of behavioral detail. It discloses the return value (repaired text and pass count), which is important for callers. It does not mention edge cases or side effects, but the core behavior is 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 exceptionally concise—just two sentences—and front-loads the purpose with examples. It contains no filler, and every clause provides value, making it easily skimmable for an agent.

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?

The description covers the core operation, return value, and common symptoms, and relies on the fully documented schema for parameter details. It omits failure behavior or when to manually set 'twice' vs 'read_as', but given the schema completeness, it is sufficiently robust for correct 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 description coverage is 100%, so each parameter (content, twice, read_as) is already fully documented with its meaning and default values. The tool description adds no extra parameter-level semantics, placing it at the baseline of 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 clearly states the tool's function with a specific verb ('Repair mojibake') and resource (text), supported by concrete symptom examples ('é', '’'). This distinguishes it from sibling tools like convert_subtitle or clean_subtitles without ambiguity.

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 strong contextual clues by listing specific symptoms of mojibake, helping an agent recognize when to apply it. However, it does not explicitly contrast with alternatives (e.g., clean_subtitles) or state when not to use it, which slightly limits guidance.

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