compute_statistic

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does add useful semantics for stats like positive_count and null_count, but it does not explicitly state that the operation is read-only or describe how missing values or sampling affect results.

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 a single concise sentence followed by a list of supported stats. Every element is relevant and the main purpose is front-loaded. No filler or redundancy.

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 output schema covers return values, but the description omits the meaning of the sample parameter and does not mention column type requirements or missing-value handling. This leaves some ambiguity for an agent trying to call the tool correctly with all parameters.

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 does define the stat parameter and clarifies 'column', but it leaves session_id and sample unexplained. This partial coverage is helpful but not complete.

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 states a specific verb and resource: 'Compute a specific statistical metric on a column' and enumerates all supported metrics. This clearly distinguishes it from broader sibling tools like describe_dataset or frequency_analysis by emphasizing single-column statistical metrics.

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 when to use the tool: whenever a specific statistic on a column is needed. However, it does not explicitly contrast it with alternatives or state when not to use it, leaving the selection decision to inference.

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