get_summary_statistics

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

No annotations are provided, so the description carries the full burden. It transparently discloses the return contract: a JSON string with 'describe' and 'correlation' keys, per-column stats, and Pearson correlation. Edge cases like missing datasets or non-numeric columns are not mentioned, but the core behavior is clear.

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 compact and well-structured with Args/Returns sections. The purpose and usage guidance are front-loaded, and every sentence contributes meaning without 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?

For a one-parameter read-only tool with an output schema, the description covers purpose, timing, parameter semantics, and return shape. It lacks only minor details such as the source of valid dataset names and error behavior, but nothing essential is missing.

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's Args section supplies the only meaning: dataset is 'Name of the dataset.' This is minimally adequate for a single required string parameter, but it does not explain where to find dataset names or the expected format.

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 opens with a specific verb ('Return') and resource ('descriptive statistics and a correlation matrix for a dataset'). It distinguishes this tool from sibling chart generators by positioning it as a pre-chart analysis step.

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 explicitly states when to call this tool: 'Call this before generating charts to understand distributions, spot outliers, and find which numeric columns correlate strongly.' It gives clear context and intent, though it does not explicitly name alternatives or provide when-not-to-use conditions.

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