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, and it discloses nothing. It does not state input format, expected output, formula conventions (e.g. z vs t), or any constraints.

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?

It is short, but the brevity comes from under-specification rather than economy. There is nothing front-loaded because there is no content.

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 two parameters, no annotations, and no output schema, the description should explain inputs, statistical interpretation, and return shape. It supplies none of these, leaving the agent unable to call the tool 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?

Schema description coverage is 100%, which sets the baseline at 3 per the rubric. However, the schema descriptions themselves ('Z to process', 'Values to process') are uninformative placeholders, so the description adds no compensating meaning.

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 is the tautology 'Margin Of Error: Margin Of Error', merely restating the tool name with no verb, resource, or scope. It does not tell the agent what computation is performed or distinguish it from statistical siblings like x402-confidence-interval-mean or x402-standard-error.

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?

There is no guidance on when to use this tool, when not to, or which sibling to prefer. The agent is given no condition under which margin-of-error computation is the right choice.

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