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profile_dataset

Profile tabular datasets to reveal shape, data types, null percentages, unique counts, and distributions. Also scans for PII column names without modifying the source file.

Instructions

Profile a tabular dataset: shape, dtypes, null %, unique counts, distributions.

Supports CSV, TSV, Excel (.xlsx/.xls), SPSS (.sav), Stata (.dta).
Performs PII column name scan before profiling (non-blocking, annotated).
Never modifies the source file.

Args:
    path:        Absolute local path to the dataset file.
    sample_rows: If > 0, include this many rows as a data sample in the output.

Returns JSON with: path, rows, columns, null stats, duplicate count,
per-column profile (dtype, nulls, distributions or top values),
and any flagged PII column names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
sample_rowsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It clearly states the tool is non-destructive ('Never modifies the source file'), performs a PII scan before profiling, and returns a JSON with specific fields. This provides good behavioral context beyond what annotations would offer.

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

Conciseness4/5

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

The description is well-structured with a summary line, format support, behavioral notes, and an Args section. It is concise without wasted words, though some information (like 'never modifies' could be integrated without repetition.

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 tool's complexity (2 parameters, no annotations, but a clear output schema mentioned), the description covers all necessary aspects: parameter semantics, supported formats, behavioral notes, and output structure. It is complete for an agent to use confidently.

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

Parameters4/5

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

The description adds meaning beyond the input schema: for 'path' it specifies 'Absolute local path', and for 'sample_rows' it explains the condition (if > 0) and effect on output. With 0% schema description coverage, the description compensates well.

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

Purpose4/5

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

The description uses 'Profile a tabular dataset' as a specific verb and resource, and lists the outputs (shape, dtypes, null %, unique counts, distributions). It clearly conveys the tool's purpose but does not explicitly differentiate it from sibling tools like clean_dataset or anonymize_text.

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 usage for exploratory data analysis but lacks explicit guidance on when to use this tool versus alternatives. It states 'Never modifies the source file' which helps in selecting it for read-only operations, but no exclusions or specific context 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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