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

Annotations already declare this as a safe, read-only, idempotent operation, so the description need not restate those traits. It adds value by specifying the exact outputs (comments, blank lines, density) and supported language families, which informs the agent about the tool's capabilities beyond the schema.

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 compact two-sentence summary: the first sentence states the core function and output metrics, the second lists supported languages. There is no fluff or redundant information; every sentence contributes to understanding the tool.

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?

With a read-only annotation, a well-defined schema, and an output schema present, the description covers the essential context: what the tool counts, which languages it supports, and the output metrics. For a simple analysis tool of this complexity, no additional behavioral details are necessary.

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?

Both parameters are fully described in the input schema with clear descriptions (source code string and optional language hint), providing 100% schema coverage. The tool description does not add additional parameter semantics, so the baseline score of 3 is appropriate.

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 opens with 'Count lines of code' and enumerates the specific metrics (total, code, comment, blank, density), making the tool's function unambiguous. It also lists supported languages, which distinguishes it from generic text analysis tools like text_stats or count_tokens.

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 implies usage for code analysis but does not explicitly state when to use it over alternatives or when not to use it. There is no mention of sibling tools such as count_tokens or calculate_readability, leaving the choice to the agent's inference.

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