rolling_std

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. It only states what the tool does without describing how it behaves: no information on edge cases (e.g., window size larger than data length), performance characteristics, error handling, or output format. This is inadequate for a tool with parameters and no output schema.

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 extremely concise and front-loaded in a single sentence: 'Compute rolling standard deviation over a window (Domain: timeseries, Category: analysis)'. Every word serves a purpose, with no wasted text, making it efficient and easy to parse.

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 with parameters), lack of annotations, and no output schema, the description is incomplete. It fails to explain behavioral aspects, parameter details, or return values, leaving significant gaps for an AI agent to understand and invoke 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?

The input schema has 2 parameters with 0% description coverage, so the description must compensate. It mentions 'window' but does not explain its semantics (e.g., integer representing the rolling window size) or the 'data' parameter (e.g., array of numbers for time-series). The description adds minimal value beyond the schema's structural information.

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 the tool's purpose: 'Compute rolling standard deviation over a window' with the domain 'timeseries' and category 'analysis'. It specifies the verb ('compute'), resource ('rolling standard deviation'), and scope ('over a window'), but does not explicitly differentiate from sibling tools like 'rolling_variance' or 'standard_deviation'.

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 minimal usage guidance with the domain 'timeseries' and category 'analysis', implying it's for time-series data analysis. However, it offers no explicit guidance on when to use this tool versus alternatives like 'rolling_variance', 'standard_deviation', or other statistical tools in the sibling list, nor does it mention prerequisites or exclusions.

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