partial_autocorrelation

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 states what the tool computes but does not describe output format, error conditions, performance characteristics, or any side effects. For a statistical analysis tool with no annotations, this lack of behavioral details is a significant gap, leaving the agent with incomplete operational understanding.

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 and front-loaded, stating the core function in the first phrase. The additional context (domain and category) is brief and relevant. There is no wasted verbiage, making it efficient, though it could be slightly more structured (e.g., separating usage notes).

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 complexity of a statistical analysis tool, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on output format, error handling, and practical usage, which are essential for an agent to use the tool effectively. The minimal information provided does not suffice for the tool's context.

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 0% description coverage, so the description must compensate. It mentions 'data' and 'lag' implicitly but does not explain their semantics, constraints, or examples (e.g., data as a time series array, lag as a positive integer). Without this added meaning, the parameters remain poorly defined, hindering correct invocation.

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 partial autocorrelation at a given lag' with the clarifying phrase 'correlation after removing effects of intermediate lags'. It specifies the domain (timeseries) and category (analysis), making the intent unambiguous. However, it does not explicitly differentiate from its sibling 'autocorrelation', which is a closely related tool, so it falls short of a perfect score.

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 the domain (timeseries) and category (analysis), but does not specify scenarios, prerequisites, or comparisons to sibling tools like 'autocorrelation'. Without explicit usage instructions, the agent must infer context, which is insufficient for effective tool selection.

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