differencing

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 'apply differencing' which implies a transformation operation, but doesn't describe what the transformation entails (e.g., whether it modifies data in-place, returns a new series, handles edge cases like missing values, or has specific requirements like data length). For a tool with no annotations and two parameters, 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 in a single sentence, with no wasted words. It efficiently states the purpose and includes domain/category context. However, the brevity comes at the cost of completeness, as noted in other dimensions.

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 no annotations, 0% schema description coverage, no output schema, and two parameters, the description is incomplete. It lacks details on parameter semantics, behavioral traits (e.g., how differencing is applied, error handling), and output expectations. For a transformation tool in a domain like time series analysis, more context is needed to guide effective use.

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 neither parameter ('data' and 'lag') has descriptions in the schema. The tool description adds no information about parameters—it doesn't explain what 'data' should contain (e.g., a time series array), what 'lag' means (e.g., the order of differencing), or any constraints (e.g., lag must be positive, data must be numeric). With two undocumented parameters, the description fails to compensate for the schema 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 clearly states the tool's purpose: 'Apply differencing to make time series stationary' with domain and category context. It specifies the action ('apply differencing') and the goal ('make time series stationary'), which is more specific than just restating the name. However, it doesn't explicitly differentiate from potential sibling tools that might also handle time series stationarity (like 'detrend' or 'stationarity_test'), though those are distinct operations.

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-to-use, when-not-to-use, or comparison to sibling tools (e.g., 'detrend' for removing trends or 'stationarity_test' for checking stationarity). Usage is implied by the purpose statement alone.

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