exponential_smoothing

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states the tool performs a 'forecast,' implying a read-only operation that generates predictions, but does not disclose behavioral traits such as output format, error handling, computational limits, or assumptions (e.g., stationarity). The description is too vague to inform the agent about how the tool behaves beyond its basic purpose.

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 extremely concise: a single sentence with no wasted words. It is front-loaded with the core purpose and includes domain/category context efficiently. Every part of the description adds value, making it easy to parse quickly.

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 (a forecasting tool with 3 parameters), no annotations, 0% schema coverage, and no output schema, the description is incomplete. It states what the tool does but lacks essential details: parameter explanations, behavioral context, output information, and usage guidelines. For a tool with undocumented inputs and no structured output, more descriptive content is needed.

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 schema description coverage is 0%, meaning parameters are undocumented in the schema. The description does not mention any parameters or their semantics (e.g., what 'alpha' or 'forecast_periods' represent). It fails to compensate for the lack of schema documentation, leaving the agent with no guidance on input meaning beyond the raw schema structure.

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: 'Simple exponential smoothing forecast for time series' specifies the verb ('forecast'), resource ('time series'), and method ('simple exponential smoothing'). It distinguishes from siblings by mentioning the domain ('timeseries') and category ('analysis'), but does not explicitly differentiate from similar forecasting tools like 'holt_winters_forecast' or 'moving_average_forecast'.

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 minimal guidance: it mentions the domain ('timeseries') and category ('analysis'), implying usage for time series forecasting. However, it lacks explicit when-to-use instructions, prerequisites, or alternatives (e.g., when to choose this over other forecasting methods like Holt-Winters). No exclusions or comparisons to sibling tools are provided.

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