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register_data_dictionary

Record a dataset's variable definitions—names, types, labels, units, and unique values—for consistent reuse in analysis. Re-registering the same dataset replaces its previous dictionary.

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
Behavior5/5

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

No annotations are provided, so the description bears full burden. It fully discloses key behaviors: idempotent replacement ('replaces the previous dictionary'), skipping entries without a name, and returning a confirmation message with count or error. No hidden side effects are omitted.

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 about 12 lines, starting with a bold purpose, then a brief explanatory sentence, followed by a structured Args block, and ending with a Returns note. Every sentence adds value, no redundancy, and the structure is easy to scan.

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

Completeness5/5

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

Given the tool's complexity (4 parameters, nested objects in variables, requirement for at least one valid entry), the description covers input structure, behavior on re-registration, error handling (skipping invalid entries, error if none provided), and expected output. It is complete for an agent to select and invoke correctly.

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 description coverage is 0%, so the description must compensate. The Args section explains all four parameters in detail: 'dataset_name' as a string example, 'variables' as a list of plain strings or objects with optional fields (with note about skipping unnamed entries), 'project_id' as key component, 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.

Purpose5/5

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

The description opens with 'Record a dataset's data dictionary — one stored entry per variable', using a specific verb ('record') and resource ('data dictionary'). It clearly distinguishes this tool from siblings like 'profile_dataset' or 'record_dataset_treatment' by focusing on capturing variable metadata for reuse.

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

Usage Guidelines4/5

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

The description explains the purpose: capturing variable names, types, labels, etc., so future scripts reuse exact names and treatments. It also notes idempotency (re-registering replaces previous). However, it does not explicitly state when not to use this tool or compare it to similar siblings like 'add_glossary_term'.

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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