Profile a data file: structure, types, quality warnings and next steps.

Reads only the first rows for efficiency and returns file info, a per-column profile (dtype, null counts, cardinality, sample values and numeric min/max/mean), data-quality warnings, and suggested pandas operations to run next with run_pandas_code. This is the recommended first call when exploring an unknown dataset.

Return the complete value distribution of one or more columns.

For each requested column, reports dtype, total/null/unique counts and the value frequencies (sorted most-common first). Unlike read_metadata, this scans the whole file rather than a sample, so it is ideal for understanding categorical columns. Frequencies are capped at 200 distinct values per column.

Execute pandas code in a restricted sandbox and return result.

pd (pandas) and np (numpy) are in scope; when file_path is given, the file is loaded into a DataFrame named df. The code must assign its output to result. For safety, filesystem/process/interpreter access (import, open, exec, eval, os/sys/…) is rejected. DataFrame/Series results are returned as records.

Generate an interactive Chart.js HTML file from series data.

Supports bar, line and pie charts. The self-contained HTML is written to the charts directory (MCP_CHARTS_DIR, default ./charts) and the tool returns its path. Pie charts use the first series only. Feed it aggregated data — e.g. the output of a run_pandas_code group-by.