distribution_analysis

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

With no annotations, the description must carry behavioral disclosure, but it only names three outputs. It does not mention how columns or bins are interpreted, what happens with non-numeric columns, missing values, or whether the operation is read-only.

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 text is a compact, front-loaded phrase with no filler; every word adds information. It is concise, even if the content is thinner than an agent likely needs.

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?

An output schema exists, so the return values are partly covered, but the description omits parameter semantics, usage conditions, and sibling differentiation. For a 3-parameter tool in a large family of analysis tools, this is not complete enough to invoke confidently.

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% and the description does not explain table_name, columns, or bins. The mention of histogram hints that bins relate to binning, but the parameters' meaning and defaults are not spelled out.

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 clear purpose: analyzing the distribution of data, and lists concrete outputs (histogram, skewness, kurtosis). It is identifiable among siblings like correlation_analysis or statistical_test, though it does not explicitly name alternatives or boundaries.

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?

No guidance is given on when to choose distribution_analysis over similar tools such as profile_data, statistical_test, or detect_anomalies. There are no conditions, prerequisites, or exclusions.

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