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 computational tool with no annotations, this is a significant gap in transparency about how the tool behaves beyond its basic function.

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, consisting of a single sentence that directly states the tool's function. There is no wasted verbiage, and it efficiently communicates the core purpose. However, it could be slightly improved by structuring into multiple sentences for clarity, but it's still highly efficient.

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 has 2 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It does not explain parameter semantics, output format, or behavioral details. For a computational tool in a domain like timeseries analysis, more context is needed to ensure the agent can use it effectively without guesswork.

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 0%, meaning the input schema provides no descriptions for parameters 'data' and 'lag'. The description does not compensate by explaining what 'data' should contain (e.g., a time series array) or the meaning of 'lag' (e.g., integer shift). Without this, the agent lacks semantic understanding of the parameters, making it hard to use correctly.

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 autocorrelation at a given lag - measures correlation between series and itself shifted by lag periods'. It specifies the verb ('compute'), resource ('autocorrelation'), and provides a concise mathematical definition. However, it does not explicitly differentiate from sibling tools like 'correlation' or 'partial_autocorrelation', which would be needed for 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'), which implies a context, but does not specify scenarios, prerequisites, or comparisons to similar tools like 'correlation' or 'partial_autocorrelation' in the sibling list. This leaves the agent without explicit usage instructions.

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