rolling_variance

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. The description only states what the tool does without any information about side effects, performance, error handling, or output format. For a computational tool with no annotation coverage, this is a significant gap in transparency.

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 very concise with a single sentence and domain/category tags, making it front-loaded and efficient. However, the brevity leads to under-specification rather than optimal conciseness, as it lacks necessary details for full understanding.

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 tool's complexity (statistical computation), lack of annotations, no output schema, and low schema description coverage, the description is incomplete. It doesn't cover behavioral aspects, parameter details, or output expectations, making it inadequate for effective tool selection and invocation.

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 input schema has 2 parameters with 0% description coverage, meaning the schema provides no semantic information. The description mentions 'window' but doesn't explain what 'data' represents or the expected format (e.g., time series array). It adds minimal value beyond the schema, failing to compensate for the coverage gap.

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 states the tool's purpose as 'Compute rolling variance over a window' with domain/category tags, which is clear but vague. It specifies the verb ('Compute'), resource ('rolling variance'), and scope ('over a window'), but doesn't differentiate from sibling tools like 'variance' or 'rolling_std' beyond the domain/category hints. This makes it adequate but not specific enough for sibling distinction.

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 no guidance on when to use this tool versus alternatives. It mentions 'Domain: timeseries, Category: analysis', which implies context but doesn't specify scenarios, prerequisites, or exclusions. With many sibling tools for statistical analysis, this lack of explicit usage guidelines leaves the agent without clear direction.

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