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

No annotations are provided, so the description must fully disclose behavior. It mentions 'detect and normalize' but does not clarify the output format (e.g., whether it returns the normalized string, or detection results). The tool's side effects (none) and performance 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 very short (two sentences) and avoids redundancy in terms of word count. However, the second sentence ('When: Detect/normalize CRLF vs LF.') repeats some ideas. It is concise but not optimally structured for clarity.

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 low schema coverage, no output schema, and the tool's simplicity (line ending normalization), the description is incomplete. It fails to specify the return value, error cases (e.g., invalid style parameter), or edge cases (e.g., empty input). An agent would lack confidence in using it correctly.

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 0%, so the description should compensate. It mentions the enum values (lf/crlf/cr) for the style parameter but does not describe the required text parameter or how they interact (e.g., default behavior when style is omitted). The description adds minimal semantics 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?

The description clearly states it detects and normalizes line endings to lf/crlf/cr. This distinguishes it from sibling tools like unicode_normalize which handle Unicode normalization. However, it could be more explicit about the text resource (e.g., 'normalizes line endings in a text string').

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 includes a 'When:' hint: 'Detect/normalize CRLF vs LF.' This implies usage context for detecting CRLF vs LF inconsistencies. However, it does not explicitly state when not to use this tool or mention alternatives among siblings.

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