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

With no annotations, the description carries the full behavioral burden. It does disclose the key behavioral choice (sample variance, not population) and that it returns a value, but it omits format and edge-case behavior such as how arrays are encoded or how empty or invalid inputs are handled.

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?

Two short sentences with no filler; the core purpose and result are front-loaded. Every clause earns its place.

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?

Adequate for a trivial calculation tool, but incomplete given the crowded sibling space: it does not specify input format, required versus optional parameters, or how it differs from x402-variance-sample and x402-variance-population. There is no output schema, so return details must be inferred.

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 coverage is 100%, so the baseline is 3 even though the schema descriptions ('Data to process', 'Sample to process') are low-value. The description adds 'values or data array' but does not clarify which parameter to use, the expected separator, or whether data and sample are alternatives.

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?

States a specific operation — calculate variance of a set of numbers — and clarifies that it returns sample variance, which distinguishes it from population-variance tools. It does not, however, differentiate it from the nearly identical x402-variance-sample sibling.

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?

Provides no guidance on when to choose this tool over alternatives such as x402-variance-population or x402-variance-sample. The only instruction is how to supply input ('values or data array'), not the conditions under which this tool should be selected.

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