calculate_z_factor

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

No annotations are provided, so the description carries the full burden of explaining behavior. It states that the tool 'validates' metrics, but does not disclose what validation involves, whether a Z-factor value is returned, how edge cases are handled, or what errors might occur. This is too vague for an agent to predict the tool's actual behavior.

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 filler. It is front-loaded with the key concept of validation, though this comes at the cost of specificity. It is appropriately sized for a short description, but could be slightly expanded to include the actual output metric without losing conciseness.

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 no annotations, no output schema, and two unannotated parameters, the description leaves significant gaps. It does not state the return value (presumably the Z-factor), how success is measured, or any details about the calculation context. In the presence of related sibling tools, this description is insufficient for an agent to invoke the tool correctly and interpret its result.

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 lists two array parameters without descriptions, giving 0% schema coverage. The description adds only that positive and negative controls are involved, but does not explain what the values represent, expected array lengths, units, or how the arrays are used in the calculation. This minimal information does not sufficiently compensate for the missing schema documentation.

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 says 'Validates microplate HTS metrics' but does not specifically mention calculating the Z-factor, which is the tool's clear purpose implied by its name. The verb 'validates' is ambiguous and does not distinguish this tool from siblings like assess_parallelism or detect_assay_outliers. A more precise description such as 'calculates the Z-factor from positive and negative control measurements' would be clearer.

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?

There is no guidance on when to use this tool versus alternatives. The description only indicates that it operates on positive and negative controls, but does not state the broader context (e.g., assay quality assessment) or compare against sibling tools. No exclusions or prerequisites are mentioned.

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