simple_moving_average

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 mentions the SMA computation method but lacks critical behavioral details: it doesn't specify what happens with insufficient data (e.g., if window > data length), whether it returns an array of same length or truncated, error handling, or performance characteristics. This is inadequate for a tool with parameters and no annotations.

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 and front-loaded: a single sentence defines the tool's purpose, followed by domain/category tags. There is no wasted verbiage; every element serves a clear purpose, making it efficient and well-structured.

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 (mathematical computation with parameters), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It omits essential details: parameter semantics, behavioral traits (e.g., handling edge cases), and expected output format. This leaves significant gaps for an AI agent to use the tool correctly.

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. It only partially explains parameters: 'window' is described as the number of last values, but 'data' is not explained at all (e.g., expected format, numeric constraints). The description adds minimal meaning beyond the bare schema, failing to address the coverage gap adequately.

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 Simple Moving Average (SMA) - average of last 'window' values at each point.' It specifies the verb ('compute'), resource ('Simple Moving Average'), and includes domain/category context. However, it does not explicitly differentiate from sibling tools like 'moving_average' or 'exponential_moving_average', 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 implies usage context with 'Domain: timeseries, Category: analysis', suggesting it's for time series analysis. However, it provides no explicit guidance on when to use this tool versus alternatives like 'moving_average' or 'exponential_moving_average', nor does it mention prerequisites or exclusions, leaving usage somewhat vague.

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