profile_dataset

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

With no annotations, the description carries the transparency burden. It explicitly specifies the outputs (shape, dtypes, numeric stats, sample rows) and the constraint that filename must be a CSV inside datasets/, which adds meaningful behavioral context. However, it does not mention side effects (e.g., read-only behavior) or error handling, leaving some aspects implicit.

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 concise and front-loaded with the main action, followed by a minimal Args block. Every sentence adds value—there is no fluff or repetition of schema fields, making it an efficiently structured docstring.

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 simple profiling tool, the description covers purpose, parameters, and output details. The presence of an output schema handles return structure, and the description adds the necessary file-location constraint. This is complete enough for an agent to select and invoke the tool correctly.

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?

Both parameters are explained in the Args section with more context than the schema: filename specifies a CSV inside datasets/ with an example, and sample_rows specifies how many head rows to include. Since schema descriptions are absent (0% coverage), the description fully compensates by providing essential meaning beyond the schema.

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 'Profile a dataset' and enumerates exactly what profiling includes: shape, dtypes, numeric stats, and row sample. This clearly distinguishes it from sibling tools like list_datasets (which lists datasets) or plot_distribution (which visualizes), making the tool's purpose unmistakable.

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 explicit usage context is provided—the description doesn't say when to choose profiling over detect_missing_values or list_datasets. The purpose implies initial data exploration, but there is no when/when-not guidance or named alternatives, leaving the usage scenario to be inferred.

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