z_test

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 behavioral disclosure. While it describes what the tool does (perform a z-test), it doesn't mention important behavioral aspects such as what the output format will be (since no output schema exists), whether it returns p-values, test statistics, or confidence intervals, or any assumptions about the data (e.g., normality). This leaves significant gaps in understanding how the tool behaves.

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 and front-loaded with the core purpose in a single sentence. It efficiently communicates the statistical test and its application without unnecessary details. However, it could be slightly more structured by explicitly separating the purpose from domain/category information.

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 a statistical hypothesis test with 4 parameters, 0% schema description coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain parameter meanings, output format, or key assumptions (e.g., data should be normally distributed for z-test validity). For a tool with this level of complexity and poor structured documentation, the description should do much more to compensate.

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%, meaning none of the parameters (data, population_mean, population_std, alpha) have descriptions in the schema. The tool description doesn't provide any additional information about what these parameters mean, their expected formats, or typical values. For example, it doesn't clarify that 'alpha' is the significance level or that 'data' should be a numeric sample array. This leaves parameters largely undocumented.

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: 'Perform z-test for known population standard deviation to test whether sample mean differs from population mean.' It specifies the statistical test (z-test), its domain (statistics), and category (inference). However, it doesn't explicitly differentiate from sibling tools like 't_test_one_sample' or 'z_scores', which are related statistical tools.

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 and category but doesn't specify when to use this tool versus alternatives like t-tests (when population standard deviation is unknown) or other hypothesis tests. No explicit when/when-not instructions or prerequisite conditions are provided.

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