register_data_dictionary

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

No annotations are provided, and the description does not disclose behavioral traits such as whether it overwrites existing entries, idempotency, or what happens if a dataset already has a dictionary. The description states what it records but not side effects or permissions.

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 with a clear front-load: a one-sentence summary followed by a bulleted explanation of parameters. Every sentence adds value, though the args list is standard docstring format.

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 input well but does not explain what the tool returns (output schema exists but unused). It is adequate for a registration tool but could be more complete with return value details.

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?

With 0% schema description coverage, the description compensates by explaining each parameter's purpose: dataset_name with an example, variables as a list of objects with required name and optional fields, and the optional project_id and dataset_path. This adds significant meaning beyond the bare 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 clearly states it records a dataset's data dictionary with one entry per variable, specifying that it captures names, types, labels, unique values, and units. It is specific and distinct from siblings like 'record_dataset_treatment', though not explicitly differentiating.

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 implies usage for standardizing variable names for future scripts ('so future scripts use the exact same names and treatments'), but it does not provide when-not-to-use guidance or mention alternatives.

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