lagged_values

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 the tool creates lagged versions by shifting values, which implies a read-only transformation, but doesn't clarify if it modifies input data, handles edge cases (e.g., missing values at boundaries), or describes output format. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 with the core purpose in a single sentence, followed by domain/category context. There's no wasted verbiage, and every part contributes to understanding. However, the domain/category annotation could be considered slightly redundant if already inferred from context, but it's still 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's complexity (time series transformation), lack of annotations, no output schema, and low schema coverage (0%), the description is incomplete. It doesn't explain the output (e.g., format, handling of shifted values), error conditions, or practical usage examples. For a 2-parameter tool with no structured support, this leaves the agent under-informed.

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%, so the description must compensate for undocumented parameters. It mentions 'shift values by specified number of periods', which hints at the 'lags' parameter's purpose, but doesn't explain 'data' (e.g., expected format, time series requirements) or provide details on 'lags' (e.g., valid ranges, multiple lag support). This adds minimal semantic value beyond the bare schema.

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: 'Create lagged version of time series - shift values by specified number of periods'. It includes a specific verb ('Create'), resource ('lagged version of time series'), and operation details. However, it doesn't explicitly differentiate from sibling tools like 'differencing' or 'autocorrelation' that might also manipulate time series data, which prevents 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 offers no explicit when/when-not instructions or references to sibling tools like 'differencing' or 'moving_average' that might serve similar purposes in time series analysis.

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