data_builtin

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not explain side effects, whether the data is returned or assigned to the session, error behavior, or what happens if the package is not installed. The examples hint at usage but do not disclose the actual operational behavior.

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?

Two sentences with zero waste. The core action is front-loaded, and the example list is compact and informative. Every sentence earns its place.

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 description covers purpose and typical use cases, and the schema documents all parameters. However, with no annotations and no output schema, it leaves out what happens when data loads (return value vs. session assignment) and how package resolution works, so it is only minimally complete for an agent.

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 100%, so the baseline is 3. The description adds real examples for dataset/package combinations, which supports parameter understanding, but it does not explain the optional 'name' parameter beyond what the schema already states.

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 uses a specific verb-resource pairing ('Load a dataset that ships with R or with an installed package') and provides concrete examples like mtcars, iris, and sleepstudy from lme4. This makes the tool's purpose immediately identifiable and distinct from generic data-loading tools.

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?

It states clear usage context: 'Useful for demonstrating a method or checking that an analysis works before running it on real data.' It does not explicitly mention alternatives or when not to use it, but the intended scenario is clear.

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