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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context beyond that: it discloses the underlying method (n-gram frequency analysis and common word markers) and the supported language list, which sets expectations for accuracy and scope without contradicting annotations.

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 two sentences, front-loads the purpose, and contains no filler. It efficiently provides the method and supported language list, earning every sentence's place.

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?

For a simple single-parameter tool with an output schema, the description is fully complete: it explains what it does, how it works, and its language scope. The schema covers the input constraint, and annotations cover safety, so no critical information is missing.

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?

The input schema fully documents the 'input' parameter and its minimum length requirement, so the description does not need to repeat it. The description adds no additional parameter semantics beyond what the schema already provides, aligning with the baseline for high schema coverage.

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 a specific verb ('Detect') and resource ('natural language of a text'), and distinguishes itself from sibling tools like detect_secrets or toxicity_scan by specifying its unique task. It also lists supported languages, further clarifying scope.

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 by describing what it does, but does not explicitly state when to use this tool versus alternatives or mention any exclusions. There is no guidance on when not to use it or how it differs from related text analysis tools.

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