Data Profiler MCP

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Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/haiiibin-data-profiler-mcp

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What this server can do and what it asks for, from its tool list, install config and code. Checked Oct 25, 2026.

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4 read · 0 write · 0 delete · 3 not yet classified

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Python package

Tools (7)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• 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.

• compare_datasets

  Diff two tabular files: what changed between version A and version B. Reports the row-count delta, columns added or removed in B, dtype changes on shared columns, and per-column null-rate (and, for numeric columns, mean) for both files side by side. Use this to compare two snapshots of the same dataset, validate a data pipeline's output against a baseline, or check what a transformation changed.

• correlation_matrix

  Correlations between numeric columns, ranked by strength. Computes pairwise correlations across all numeric columns (``pearson`` by default; ``spearman`` or ``kendall`` for rank-based relationships) and returns pairs ranked by absolute correlation, a ``high_correlation_pairs`` list (|r| >= 0.9, a multicollinearity signal when preparing features for modeling), and the full matrix when there are 15 or fewer numeric columns. Pass ``column`` to instead rank how strongly every other numeric column correlates with that one, for example a target variable. Constant columns are excluded and listed. U…

• detect_quality_issues

  Run a focused data-quality audit and return issues grouped by severity. Detects duplicate rows, all-missing and high-missing columns, constant columns, likely identifier columns, numbers stored as text, dates stored as text, columns mixing numeric and text values, leading/trailing whitespace, and empty (whitespace-only) strings. Each issue carries a column (or ``null`` for table-level), an issue code, a severity (``high``/``warning``/``info``), and a plain-language explanation. Use this when the user cares specifically about cleanliness, is preparing data for modeling, or asks "is anything w…

• 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.

• 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…

• suggest_dtypes

  Recommend more memory-efficient or more-correct column dtypes. For each column, proposes a better dtype when one exists: text that is fully numeric to a numeric type, low-cardinality text to ``category``, and oversized integer/float columns downcast to smaller types. Reports per-column and total estimated memory savings. Use this to help a user shrink a DataFrame's memory footprint or fix columns that were loaded with the wrong type.

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Weekly downloads

63

Last commit

Oct 9, 2026 (19 days ago)

Last release

Oct 9, 2026 (19 days ago)

Last package publish

Oct 9, 2026 (19 days ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.haiiibin/data-profiler-mcp

Version

0.4.0

Auth

No auth

Packages

pypi data-profiler-mcp

License

MIT

Repository

github.com/haiiibin/data-profiler-mcp

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

Official MCP Registrygithub-graphql