calculate_lod_loq

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

No annotations are present, so the description carries full responsibility. It only states the calculation but does not disclose the formula, return format, or any assumptions about the blank responses (e.g., normality, sufficient count). This leaves a significant gap in behavioral expectations.

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 sentence, concise and front-loaded. However, it is almost tautological with the tool name, providing minimal additional value beyond what the name already conveys.

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 2-parameter calculation tool with no output schema, the description is too thin to fully prepare an agent. It does not mention what is returned (e.g., a tuple of LOD and LOQ) or any constraints on input data length. The sibling tools suggest a domain context where such details matter.

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 0%, and the description does not explain the meaning of 'slope' or the expected structure of 'blank_responses.' While the parameter names are suggestive, the description adds no semantic detail beyond the schema's bare types.

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 uses a specific verb ('Calculates') and clearly identifies the resource ('Limit of Detection (LOD) and Limit of Quantitation (LOQ)') and the input source ('background blanks'), which distinguishes it from sibling tools like calculate_4pl_curve or calculate_z_factor.

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, such as assess_parallelism or detect_assay_outliers. No context, prerequisites, or exclusions are mentioned.

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