Profiles CSV/Parquet/Excel/JSON files: schema, stats, quality flags and dtype tips for LLM agents.
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io.github.haiiibin/data-profiler-mcp is an MCP server that profiles CSV/Parquet/Excel/JSON files: schema, stats, quality flags and dtype tips for LLM agents. Its tool list has not been published yet over stdio and http, requires no API key, and scores 82/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"data-profiler": {
"command": "data-profiler-mcp"
}
}
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Profiles CSV/Parquet/Excel/JSON files: schema, stats, quality flags and dtype tips for LLM agents.
Run this in your terminal to verify the server starts. Then let us know if it worked - your result helps other developers.
uvx 'data-profiler-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
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No known CVEs.
Checked data-profiler-mcp against OSV.dev.
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An MCP server that lets an LLM understand any tabular data file: point it at a CSV, Parquet, Excel or JSON file and get schema, distributions, data-quality flags and dtype suggestions back as structured JSON.
Stop pasting df.head() and df.info() into chat. Ask your assistant "profile sales.csv" and it reads the file itself, then tells you what is in it, what is wrong with it, and how to load it more efficiently.

Works with Claude Desktop, Claude Code, Cursor, or any MCP-compatible client.
Seven focused tools, all returning clean JSON:
| Tool | What it does |
|---|---|
profile_dataset | One-call overview: shape, memory, missing-value summary, duplicate rows, a per-column summary, and plain-language quality flags. |
preview_data | The first / last / a random sample of n rows as real records. |
column_stats | Deep dive on one column: full percentiles, skew/kurtosis, outliers (IQR), a histogram, or top values + string lengths for text. |
detect_quality_issues | A data-quality audit: duplicates, high-missing and constant columns, numbers stored as text, mixed-type columns, whitespace padding, likely IDs, grouped by severity. |
suggest_dtypes | Memory-saving / type-fixing recommendations (text to numeric, low-cardinality to category, integer/float downcasting) with estimated savings. |
compare_datasets | Diff two files: added/removed columns, dtype changes, row-count delta, and per-column null-rate and mean side by side. |
correlation_matrix | Correlations between numeric columns (Pearson / Spearman / Kendall): pairs ranked by strength, multicollinearity flags at |r| >= 0.9, and target-vs-rest ranking via column. |
Supported formats: CSV, TSV, Parquet, Excel (.xlsx/.xls), JSON and JSON Lines. Large files are read up to a row cap and clearly flagged as sampled.
No dataset at hand? examples/sample.csv is a small sales export with deliberate quality issues (missing regions, a duplicate row, a constant column, whitespace padding) -- ask your assistant to "profile examples/sample.csv" and see what it flags.
No install needed to try it: open the Glama server page and use Try in Browser to call the tools against a sandbox (the repo ships examples/sample.csv at /app/examples/sample.csv to profile).
Requires Python 3.10+.
Requires MCP Python SDK 2.x (mcp>=2,<3). SDK 1.x is feature-frozen upstream and no longer supported here; 0.3.0 is the last release that ran on it.
# with uv (recommended)
uv tool install data-profiler-mcp
... [View full README on GitHub](https://github.com/haiiibin/data-profiler-mcp#readme)