文字コード・改行コード変換

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

With no annotations, the description carries the full burden. It explicitly states that mode=analyze only judges encoding/line endings, while mode=convert returns converted text and base64. This discloses the tool's primary behavioral characteristics without contradictions.

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, well-structured paragraph. It leads with the core purpose, lists concrete use cases, and then explains the modal behavior. Every sentence earns its place with 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 tool's moderate complexity (6 parameters, no output schema), the description covers the key aspects: purpose, use cases, modes, and return values for convert. It lacks mention of error handling or performance for large files, but these are not critical gaps.

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 baseline is 3. The description adds context about the purpose of the tool and the difference between modes, but it does not add significant parameter-level details beyond what the schema already provides (e.g., enum values, defaults).

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 purpose: detect and convert character encoding (UTF-8, Shift_JIS), BOM, and line endings of CSV/text. It distinguishes itself from sibling tools like contrast_check or webp_convert, which address different tasks.

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 concrete usage examples (investigating garbled CSV from clients, preprocessing form data, checking accidental line-ending changes in repositories) and explains the two modes (analyze vs convert). It does not explicitly state when not to use, but the examples are sufficiently contextual.

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