moving_average

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

No annotations are provided, so the description carries full burden for behavioral disclosure. The description only states what the tool calculates, with no information about computational characteristics (e.g., handling of edge cases, performance, memory usage), error conditions, or output format. For a statistical calculation tool with zero annotation coverage, this is a significant gap in transparency.

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 front-loads the core purpose ('Calculate moving average') and adds only essential domain/category context. Every element earns its place, making it easy for an agent 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 tool's moderate complexity (statistical calculation with two parameters), no annotations, 0% schema coverage, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how it handles invalid inputs, or any behavioral nuances. For a tool that performs calculations on data, more context about input expectations and output format 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?

Schema description coverage is 0%, meaning neither parameter ('numbers', 'window') has descriptions in the schema. The tool description adds no parameter information beyond what's implied by the tool name. It doesn't explain what 'numbers' represents (e.g., numeric array, data format) or 'window' (e.g., integer size, valid range). 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: 'Calculate moving average (rolling average) of a dataset' with domain/category context. It specifies the verb ('calculate') and resource ('moving average of a dataset'), making the function unambiguous. However, it doesn't differentiate from sibling tools like 'simple_moving_average' or 'exponential_moving_average' that appear in the list, 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. It mentions the domain ('statistics') and category ('general'), but offers no explicit when/when-not instructions, prerequisites, or comparisons to sibling tools like 'simple_moving_average' or 'exponential_moving_average'. The agent must infer usage from the name alone.

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