weighted_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 of behavioral disclosure. It mentions the tool 'applies custom weights to recent observations', which implies a calculation but does not describe output format, error handling, performance characteristics, or any constraints (e.g., weight normalization). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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, consisting of a single sentence that directly states the tool's purpose and domain. Every word earns its place with no redundancy or unnecessary elaboration, making it efficient for quick comprehension.

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 operation with two parameters), lack of annotations, no output schema, and 0% schema description coverage, the description is incomplete. It does not explain the calculation method, output format, error conditions, or practical usage examples. For a tool in a domain like timeseries analysis, this leaves too much unspecified for reliable agent invocation.

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 'custom weights' and 'recent observations', which loosely map to 'weights' and 'data' parameters, but does not explain their semantics (e.g., weight array length matching data, numerical constraints, or handling of invalid inputs). The description adds minimal value beyond the bare parameter names, failing to adequately address the coverage 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: 'Compute Weighted Moving Average (WMA) - applies custom weights to recent observations'. It specifies the verb ('Compute'), resource ('Weighted Moving Average'), and domain/category context. However, it does not explicitly differentiate from sibling tools like 'simple_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 provides no guidance on when to use this tool versus alternatives. While it mentions the domain ('timeseries') and category ('analysis'), it does not specify scenarios, prerequisites, or comparisons to sibling tools like 'simple_moving_average' or 'exponential_moving_average'. This lack of explicit usage context limits its helpfulness.

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