statistics

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

Since no annotations are provided, the description must carry the full burden of behavioral disclosure. It does reveal operation-specific parameter requirements, which is helpful, but it omits important behaviors such as the existence of the 'summary' operation, the meaning of the 'population' parameter (sample vs population variance), return value structure, and edge-case handling (e.g., empty datasets). This is a moderate amount of transparency but leaves significant behavioral gaps.

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 (two sentences) and front-loaded with the core purpose and operation list. It avoids fluff and uses clear phrasing. However, it omits the 'summary' operation from the list, which is present in the schema enum, making the description slightly incomplete but not wordy.

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 tool with 5 parameters, no output schema, and no annotations, the description provides enough to use the main operations but misses key aspects: 'summary' is not mentioned, 'population' behavior is not explained, and there is no indication of return format or edge cases. The description is adequate for basic use but not fully complete for an agent to handle all scenarios correctly.

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 schema already provides detailed descriptions for all parameters (100% coverage). The description adds minimal value beyond this: it restates that percentile needs the 'percentile' parameter and correlation needs 'dataY', which the schema already documents. It does not clarify the 'population' parameter or the 'summary' operation. With high schema coverage, the baseline is 3, and the description provides no extra semantic depth.

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 statistics on a dataset' and enumerates the supported operations (mean, median, mode, etc.), making the tool's purpose unambiguous and distinct from generic calculators like 'calculate' or 'percentage_calc'. It also provides specific operation names, so an agent can immediately understand what this tool does.

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 offers some internal guidance (e.g., 'For percentile provide the percentile parameter', 'For correlation provide dataY'), but it does not explicitly state when to use this tool versus the sibling tools (calculate, convert_units, format_number, percentage_calc). There is no exclusion rule or alternative routing, so the when-to-use context is implied rather than explicit.

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