attributes_statistics

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

Discloses the computation of statistics and default stats, but is vague about error handling (e.g., non-numeric field), performance, and side effects. No annotations present, so description carries full burden but lacks completeness.

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?

Two concise sentences. Front-loaded with the main action, followed by specific parameter details. No redundant information.

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?

Output schema exists, so the description need not detail return structure. However, missing prerequisites like layer existence and field numeric type slightly reduces completeness for a tool with no annotations.

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?

Adds meaning beyond schema: describes 'stats' as a subset of specific values with defaults, and implies 'field_name' must be numeric. Schema coverage is 0%, so description compensates well for parameter semantics.

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?

Clearly states 'Compute aggregate statistics for a numeric field', which is a specific verb and resource. Differentiates from sibling tools like attributes_unique_values and attributes_calculate.

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?

Provides details on the 'stats' parameter and default behavior, but does not explicitly guide when to use this tool versus alternatives or mention prerequisites like layer existence or field type.

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