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describe_dataset

Inspect a response file to confirm its layout, scoring, and column names, and review missing-data breakdown and subgroup sizes before running analyses. Use it first on any unfamiliar dataset.

Instructions

Load a response file and describe its shape, blanks and subgroups.

Call this FIRST on any unfamiliar file. It reports counts, the missing-data breakdown (omitted versus not-reached), and subgroup sizes, and it reproduces none of the responses themselves -- so it is safe to summarise in a conversation. Use it to confirm the layout, scoring and column names are right before running an analysis, and to check subgroup sizes before asking for DIF.

Relay the diagnostics warnings verbatim; they describe how blanks were interpreted, which changes every downstream statistic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
layoutNo'wide' -- one row per examinee, one column per item. 'long' -- one row per examinee-item response, which requires `id_column` and expects `item_id` and `response` columns.wide
scoredNoTrue if the cells already hold 0/1 item scores. False if they hold the option each examinee selected (e.g. 'A', 'C'), which requires `answer_key`. This is never inferred: a raw-option file read as scored produces a complete, plausible-looking analysis of nothing. Confirm it with `describe_dataset` before trusting any statistic.
id_columnNoName of the examinee identifier column. Naming it excludes it from the item columns; leaving it unnamed makes it an extra 'item'. Identifiers are used only for exclusion and are never returned.
answer_keyNoCorrect option for each item, in the same order as the item columns. Required when `scored=False`; ignored otherwise. Its length must equal the item count -- a key that is off by one mis-scores every item after the offset, and the result looks like a form-wide item-quality problem. One entry per item, holding the option itself -- not its position, and not the whole key as a single string.
linear_formNoTrue if every examinee saw the items in column order, which is what makes a trailing run of blanks readable as not-reached. Set False for adaptive or randomised-order delivery: position is then unknown, so all blanks are treated as omits and a warning says so.
group_columnNoName of a subgroup column (demographic or otherwise). Naming it excludes it from the item columns and reports subgroup sizes. Optional here; required by `flag_dif`.
response_fileYesPath to the response file on disk, read locally. Candidate responses are passed as a path and never inline, so that a multi-thousand-examinee file does not enter the conversation transcript.
missing_policyNoHow blank responses are scored. 'omit_incorrect_notreached_missing' (default) scores a blank mid-form as incorrect -- the examinee had the opportunity -- and excludes a trailing run of blanks as not-reached, because scoring those wrong confounds item difficulty with speededness. 'all_incorrect' scores every blank wrong, which makes end-of-form items look harder and less discriminating in proportion to how speeded the form is; use it only if the scoring rules genuinely penalise unreached items. 'all_missing' excludes every blank, which conditions p-values on having attempted the item and turns items examinees skip when unsure into easy-looking ones. The two non-default policies each raise a warning.omit_incorrect_notreached_missing

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden, and it delivers: it discloses that the tool reports counts and missing-data breakdown, reproduces none of the responses themselves, is safe to summarise in conversation, and that diagnostics warnings must be relayed verbatim because they affect every downstream statistic. This goes well beyond what the schema encodes.

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?

Three short paragraphs front-load the purpose, then the usage guidance, then the diagnostics warning. Every sentence carries operational value, and the instruction to relay warnings verbatim earns its place.

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 100% schema coverage, an output schema, and a clear description of when and how to use the tool, nothing essential is missing. The description covers the tool's role in the workflow, its safe output characteristics, and the critical warning semantics.

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

Parameters3/5

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 and the description need not restate parameter details. It does add workflow context around layout, scoring, and column names, but it does not add parameter-level semantics beyond the rich schema descriptions.

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 states a specific verb and resource ('Load a response file and describe its shape, blanks and subgroups') and defines a clear diagnostic scope: counts, missing-data breakdown, and subgroup sizes. It also distinguishes itself from the analytical siblings by positioning itself as the first call before running analysis or asking for DIF.

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?

It gives explicit usage timing: 'Call this FIRST on any unfamiliar file', 'confirm ... before running an analysis', and 'check subgroup sizes before asking for DIF'. It does not explicitly name sibling tools as alternatives or state when not to use it, but the before/analysis framing provides clear context.

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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