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data-profiler-mcp

by haiiibin
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TDQS

A4.2/5.0

Scored across 7 tools

Disambiguation4/5

Most tools target a clearly different concern: overview profiling, raw row preview, single-column statistics, quality audit, dtype suggestions, dataset comparison, and correlation analysis. There is some overlap between profile_dataset and detect_quality_issues since both surface missing-data and duplicate information, but the descriptions make the intended use cases distinct enough to avoid serious misselection.

Naming Consistency4/5

Most names follow a verb_noun pattern: profile_dataset, preview_data, detect_quality_issues, suggest_dtypes, compare_datasets. column_stats and correlation_matrix are noun-based exceptions, but they are still short, descriptive, and readable, so the overall naming is consistent without being rigid.

Tool Count5/5

Seven tools is well-scoped for a data profiling server. Each tool covers a meaningful phase of exploration—high-level profiling, previewing rows, deep-diving columns, quality checks, dtype optimization, comparison, and correlation—without redundancy or bloat.

Completeness5/5

The tool surface covers the main data-profiling lifecycle: understand overall structure, inspect actual values, drill into a specific column, audit quality issues, suggest type fixes, compare datasets, and analyze correlations. There are no obvious dead ends or critical missing operations for the stated purpose.

Maintenance

ActivityActive
ResponsivenessNo issues

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