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

With no annotations, the description must disclose behavior fully. It does not specify what constitutes alphanumeric (e.g., only letters and digits, case sensitivity), the return value, or edge cases (empty string, spaces). This is too minimal for safe agent use.

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 efficient sentence with no redundant words. It is front-loaded and immediately clear, earning its place without waste.

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 simplicity (one param, no nested objects, output schema exists), the description is minimally complete. However, it does not mention the return type or behavior, which an agent would need to interpret the output schema context. Slightly below adequate.

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 the single parameter 'text' is fully documented in the schema. The description adds no extra meaning beyond 'Text to validate', but this is adequate for a simple string parameter.

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 'Check if text is alphanumeric.' clearly states the action (check) and the subject (text for alphanumeric property). It distinguishes from sibling validation tools like validate_email or validate_date by specifying alphanumeric, though it could add 'returns true/false' for more precision.

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?

No guidance is provided on when to use this tool versus alternatives (e.g., validate_base64, validate_pattern). The description does not suggest prerequisites or context, leaving the agent without decision-making support.

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