trend_analysis

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 analysis types (trends, growth rates, etc.) but lacks details on execution behavior, such as computational requirements, output format, error handling, or any limitations (e.g., data size constraints). For a tool with no annotations, this leaves significant gaps in understanding how it operates.

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 a single, efficient sentence that front-loads the core purpose without unnecessary details. It's appropriately sized for the tool's complexity, though it could be slightly more structured by separating analysis types for clarity. Overall, it earns its place without waste.

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 (5 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral traits, output format, and usage guidelines, which are crucial for effective tool invocation. Without annotations or output schema, the description should compensate more to provide a complete 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?

Schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no specific parameter semantics beyond implying analysis of time series data, which is already covered by the schema's descriptions (e.g., 'dateColumn' and 'valueColumn'). Thus, it meets the baseline of 3 without adding extra value.

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: 'Analyze time series trends, growth rates, seasonality, and forecasting for sales and performance data.' It specifies the verb ('analyze') and resource ('time series trends... for sales and performance data'), making the function evident. However, it doesn't explicitly differentiate from sibling tools like 'statistical_analysis' or 'correlation_analysis', which might also handle time series data, so it misses full sibling distinction.

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 analyzing 'sales and performance data' but doesn't specify contexts, prerequisites, or exclusions compared to siblings like 'statistical_analysis' or 'smart_data_analysis'. Without such guidelines, users must infer usage from the purpose alone.

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