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register_data_dictionary

Record a dataset's data dictionary with variable details to ensure consistent naming and treatment across analysis scripts. Replaces any existing dictionary for the same dataset.

Instructions

Record a dataset's data dictionary — one stored entry per variable.

Captures each variable's name, type, label, unique values / factor levels,
and units so future analysis scripts reuse the exact same names and
treatments. Re-registering the same dataset is idempotent: it replaces the
previous dictionary for that dataset (matched on dataset_name + project_id).

Args:
    dataset_name: Name of the dataset, e.g. "hat_cases_2015_2023".
    variables: List of variable entries. Each entry may be a plain string
        (the variable name) or an object with any of: name (required),
        type, label, unique_values, units, notes. Entries without a name
        are skipped.
    project_id: Project this dataset belongs to (default empty string);
        also part of the key used when replacing an existing dictionary.
    dataset_path: Where the dataset lives on disk (default empty string).

Returns:
    A confirmation message with the count of variables recorded for the
    dataset, or an error if none were provided.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_nameYes
variablesYes
project_idNo
dataset_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the burden. It discloses that re-registering is idempotent (replaces previous dictionary matched on dataset_name + project_id), describes the return value, and states error behavior. It does not detail side effects, but the behavior seems straightforward and well-explained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured: a one-line summary, a clarifying paragraph, then a clear bullet list of arguments, and a return description. No unnecessary words; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of 4 parameters (including a complex array), the description covers purpose, arguments, return, and idempotency. It lacks only minor details like potential error scenarios beyond empty variables, but overall it is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description provides detailed explanations for all 4 parameters. It clarifies the variables array format (string or object with name required), the role of project_id in idempotency, and provides examples. This fully compensates for the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool records a data dictionary for a dataset, storing per-variable metadata like name, type, and labels. It differentiates from sibling tools by focusing specifically on dictionary registration and mentions idempotency, which clarifies its unique purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use when you need to record variable definitions for a dataset, but it does not explicitly state when to use this tool versus alternatives (e.g., add_glossary_term, record_dataset_treatment). No exclusions or contextual guidance are provided.

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

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