statistics

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

With no annotations, the description adds valuable behavioral context: WASM-acceleration for large datasets and the note that mode returns an array. This goes beyond the input schema by disclosing performance characteristics and return type details.

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 that pack essential information: purpose, supported operations, and key behavioral note (WASM acceleration). No wasted words.

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?

The description explains input format and one output case (mode), but lacks comprehensive output descriptions for other operations. Given no output schema, more detail on return values (e.g., float for mean, integer for min/max) would improve completeness.

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 coverage is 100%, so baseline is 3. The description adds meaningful context: clarifies that mode returns an array (single or multiple modes) and that data must be in JSON array format. This enhances the bare schema definitions.

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 explicitly states the tool calculates statistical values and lists all supported operations (mean, median, mode, etc.). It is clear and specific, distinguishing it from sibling tools like 'derivative' or 'matrix_operations' which serve different purposes.

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 for large datasets via WASM-acceleration (100+ elements), but does not provide explicit when-to-use or when-not-to-use guidance compared to siblings. No alternatives or exclusions are mentioned.

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