detect_assay_outliers

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

With no annotations, the description must fully disclose behavioral traits, but it only states the mathematical boundaries used (Grubbs and IQR). It does not clarify whether outliers are removed, flagged, or returned separately, nor does it mention edge cases like missing values or empty arrays. For a filtering operation, this lack of detail is a significant gap.

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 concise (one sentence) and front-loaded with the methods. However, it is under-specification rather than genuine conciseness: it omits critical operational details that would be necessary for correct use, so it does not earn its place as a complete summary.

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 no output schema, no annotations, and sparse parameter documentation, the description is incomplete. It lacks information on what the function returns (e.g., outlier indices, filtered data), how the method affects behavior, and any expected data format or constraints. The moderate complexity of the tool demands more detail.

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 description coverage is 0%, so the description must compensate. It mentions Grubbs and IQR but does not explain the purpose of 'replicate_values' (the only required parameter) or how the 'method' parameter selects between the two boundaries. The description adds no semantic meaning beyond what the schema already shows.

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 ('filter') and names the resource ('anomalous microplate artifacts'), so the core action is clear. It also distinguishes the tool from generic 'detect' tools by mentioning the methods (Grubbs and IQR), giving some specificity. However, it could be more explicit that this is an outlier detection tool, as 'filter' might imply removal rather than flagging.

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 given on when to use this tool versus alternatives. The sibling tools like calculate_4pl_curve or assess_parallelism suggest overlapping assay analysis contexts, but the description does not mention any conditions, prerequisites, or scenarios where this tool is preferred.

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