validate_up_to_100

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool validates numbers but doesn't describe what validation entails (e.g., format checks, existence verification), whether it's read-only or has side effects, or any performance traits like rate limits. This leaves significant gaps in understanding the tool's behavior beyond the basic input constraint.

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, clear sentence that efficiently states the tool's purpose and input constraint without unnecessary words. It's front-loaded with the essential information, making it highly concise and well-structured for quick understanding.

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 complexity (validation operation with no annotations and no output schema), the description is incomplete. It doesn't explain what 'validate' means, what the output might be, or any behavioral nuances. While the schema covers parameters adequately, the lack of output schema and annotations means the description should compensate more to provide a complete picture.

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 has 0 parameters with 100% description coverage, so the schema fully documents the lack of parameters. The description adds value by specifying that input should be 'an array of up to 100 numbers,' which provides semantic context not in the schema. However, it doesn't detail number formats or validation rules, keeping the score from being a 5.

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 states the action ('validate') and resource ('array of up to 100 numbers'), which provides a basic understanding of what the tool does. However, it doesn't differentiate from siblings like 'validate_up_to_10' or 'validate_up_to_1000' beyond the quantity limit, leaving the specific validation purpose vague. It's not tautological but lacks specificity about what 'validate' entails.

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 provides no guidance on when to use this tool versus alternatives. It mentions 'up to 100 numbers' but doesn't explain why to choose this over 'validate_up_to_10' for smaller batches or 'validate_up_to_1000' for larger ones, nor does it specify any prerequisites or exclusions. Usage is implied only by the quantity limit, with no explicit context.

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