Analyze Distribution

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

With no annotations present, the description carries the full burden. It does add real value by disclosing the return contents and the important edge-case behavior that skewness/kurtosis come back as None when not computable, but it never says whether the operation is read-only, what happens if the dataset has no numeric columns, or whether results touch the dataset.

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 core sentence is front-loaded and the return information is useful rather than padding. The raw docstring 'Args:'/'Returns:' scaffolding is slightly machine-pasted in style but stays short and readable.

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?

For a single-parameter analysis tool with no output schema, the description usefully explains the return payload, which is its main strength. It remains incomplete on failure modes, column scoping, and side-effect safety, which matters since no annotations fill that gap.

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, and it does not: 'dataset_id (str): ID of the dataset' merely restates the parameter name. It gives no format, no indication of where the ID comes from, and no note on whether the analysis covers all numeric columns or a subset.

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?

States a specific verb ('Analyze') and clearly scoped resource ('distribution, spread, and shape of numerical columns'), which lets an agent distinguish it from categorical or correlation analysis siblings by column type. It stops short of naming any sibling explicitly, so it sits just below the top mark.

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 phrase 'numerical columns' implicitly tells the agent when this applies versus tools for categorical data, but there is no explicit when-to-use/when-not-to-use statement, no prerequisites, and no guidance for mixed-type datasets. Usage is inferable rather than stated.

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