stationarity_test

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states what the tool does ('test... using basic statistical measures') without detailing what 'basic statistical measures' entail, how results are interpreted, whether it's a read-only analysis, error handling, or output format. This leaves critical behavioral aspects unspecified, making it inadequate for informed tool selection.

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 extremely concise: a single sentence that directly states the tool's purpose and includes domain/category tags. It is front-loaded with essential information and has no wasted words, making it efficient 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 complexity of statistical testing, lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It omits details on behavior, parameters, and results, leaving gaps that hinder effective tool use. While concise, it does not provide the completeness needed for a tool with analytical functions.

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 one parameter ('data') with 0% description coverage, and the tool description does not mention parameters at all. It fails to explain what 'data' should contain (e.g., a time series array), expected format, or constraints. With no parameter information in the description and low schema coverage, it does not add meaningful semantics beyond the bare schema.

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 the tool's purpose: 'Test time series for stationarity using basic statistical measures.' It specifies the verb ('test'), resource ('time series'), and method ('basic statistical measures'), and includes domain/category context. However, it does not explicitly differentiate from sibling tools (e.g., 'detect_trend' or 'differencing'), which are related but not identical, keeping it from a perfect score.

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 minimal usage guidance: it mentions the domain ('timeseries') and category ('analysis'), implying context for when to use it. However, it does not specify when to choose this tool over alternatives (e.g., 'detect_trend' or 'autocorrelation'), nor does it outline prerequisites or exclusions. This lack of explicit guidance limits its helpfulness.

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