Data Profiler MCP vs Jupyter Notebook MCP

profile_dataset

Profile a tabular data file in one call: the fastest way to understand a dataset. Reads the file at ``path`` (CSV, TSV, Parquet, Excel or JSON/JSONL, detected from the extension) and returns a structured overview: - file metadata (format, size), - shape (row and column counts, and whether the profile was sampled), - total memory footprint, - a missing-value summary and a duplicate-row count, - a per-column summary (dtype, inferred type, null %, unique %, sample values, and basic stats for numeric/datetime columns), and - a list of plain-language data-quality flags. Use this first whenever a user points you at a data file and wants to know what is in it. ``max_rows`` caps how many rows are read (default: up to one million); the result flags when the file was larger and the stats are a head sample. Pass 0 to remove the cap entirely.

preview_data

Peek at actual rows of a data file. Returns ``n`` rows (capped at 100) as records. ``mode`` selects which rows: ``head`` (default), ``tail``, or ``sample`` (random). Use this to see real example values rather than just statistics, for example to check formatting, encodings, or how a specific column looks in practice.

column_stats

Deep statistical dive on a single column. For numeric columns: min/max, mean, std, a full set of percentiles (p1/p5/q1/median/q3/p95/p99), skewness, kurtosis, zero and negative counts, an IQR-based outlier count with bounds, and a 10-bin histogram. For datetime columns: the min and max timestamp. For text/categorical columns: the top values with counts and percentages, plus string-length statistics. Reach for this after ``profile_dataset`` when one column needs closer inspection. Raises an error listing the available columns if ``column`` is not found.