validate_pattern

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

The description states it returns true/false and validates format, which is transparent for a simple predicate. The annotation indicates non-destructive nature. No contradictions with 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 short but somewhat redundant: the first sentence restates the return type, and the second sentence repeats the validation purpose. Could be merged into one concise sentence.

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 validation tool with complete schema and no output schema, the description is minimally adequate. However, it lacks information on error behavior, format strictness, or any edge cases.

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%, but the description adds value by explicitly naming the supported formats (email, URL, phone) and clarifying that the output is a boolean. This goes beyond the schema descriptions.

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 that the tool validates a single string against email, URL, or phone formats, returning a boolean. This differentiates it from sibling tools extract_pattern (likely extracts parts) and mask_sensitive_data (likely masks patterns).

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 the tool is for validation of specific formats, but it does not explicitly state when to use this tool versus the siblings or when not to use it. No alternatives are mentioned.

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