descriptive_statistics

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

No annotations are present, so the description carries the full burden. It discloses an output dictionary and implies a read-only computation, but it does not specify edge-case behavior (e.g., empty data, non-numeric values) or the exact measures computed ('various statistical measures' is vague). This is a moderate disclosure, not misleading but incomplete.

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 compact with a clear structure: purpose, args, returns. It uses a common docstring format that is easy to parse, and every sentence is relevant. No fluff or redundancy.

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 output schema, the description should explain the return structure, but 'Dictionary with various statistical measures' is insufficient. It lacks details about the keys, error handling, and when to choose this over sibling tools. The single parameter is simple, but the lack of returned-key information leaves the agent underinformed.

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 description's 'data: List of numerical values' essentially repeats the schema's array-of-numbers type without adding constraints like minimum length, allowed values, or formatting. Since schema coverage is reported as 0%, the description was expected to compensate, but it fails to provide any additional semantic meaning beyond the 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 'Calculate comprehensive descriptive statistics for a dataset', which uses a specific verb (calculate) and resource (dataset). It distinguishes from sibling tools like correlation_analysis, regression, and probability tests by focusing on descriptive statistics. The return type is also mentioned, reinforcing its purpose.

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 provided on when to use this tool versus alternatives. The description does not mention any context, exclusions, or differences from sibling tools such as data_summary_from_csv_text or hypothesis_test_ttest. This is a clear gap for an AI agent deciding which tool to invoke.

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