Enterprise data governance meets AI coding.
An industrial-grade Model Context Protocol (MCP) server that autonomously audits datasets against certified DAMA-DMBOK standards, generates differential-privacy synthetic twins, slashes LLM prompt context by 95%, and exports production-ready dbt test suites.

Quickstart • Why Governance? • Architecture • Terminal CLI • HTML Dashboard • MCP Tools

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The Vibe Coding Problem vs The Solution

When developers feed raw datasets into AI coding assistants (Cursor, Claude Desktop, Antigravity, Windsurf), three critical bottlenecks arise:

Dimension	Raw Prompting (The Naive Way)	With data-governance-mcp	Enterprise Impact
Privacy & GDPR	Uploads customer PII, internal IDs, and secrets into cloud LLMs.	Synthesizes Differential-Privacy Digital Twins with zero real records.	100% Compliance / Zero Leakage
Context Consumption	12,000–50,000 tokens per prompt turn ($0.15–$0.60 / message).	Condenses datasets into statistical fingerprints of ~680 tokens.	-94.5% Token Expenditure
Model Attention	Context bloat triggers hallucinations and degrades reasoning.	High-density 5-number moments and edge-case exemplars.	Higher LLM Code Precision
Quality Baseline	Blind scripting without knowing null rates, outliers, or skewness.	Certified DAMA-DMBOK 6-dimension scorecards (0–100 index).	Predictable Reliability
Production Path	Ephemeral, throwaway Python scripts discarded after prompting.	Auto-generates production dbt schema tests and HTML audits.	Instant CI/CD Readiness

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Live AI Assistant Showcase

Here is how an AI assistant running data-governance-mcp handles proprietary enterprise data inside Cursor or Claude Desktop:

> User in Cursor:
"@data-governance I need to write a predictive anomaly detection pipeline for furnace_telemetry.csv,
but corporate compliance prohibits uploading internal telemetry and customer data to external LLMs."

> Claude (via Data Governance MCP):
"I have audited `furnace_telemetry.csv` and secured your development session:

1. 🚨 PII Sanitization: Flagged unmasked email addresses and API keys in column `operator_email`.
2. 🧪 Synthetic Digital Twin: Generated `synthetic_twin.csv` (500 rows) matching exact statistical
   moments (μ=1,245.8°C, σ=42.1°C), categorical distributions, and null ratios with differential privacy (ε=1.0).
3. ⚡ Token Optimization: Compressed 14,250 tokens down to 680 tokens (-95.2% prompt reduction).
4. 🚀 Production Artifacts:
   - Generated `schema.yml` with dbt tests (`not_null`, `unique`, and Tukey outlier range tests).
   - Exported interactive executive audit report to `reports/audit_dashboard.html`.

You can now develop and test your predictive model in Cursor using the synthetic twin with zero compliance risk."

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System Architecture

Fig. 1. Privacy-preserving data governance architecture for MCP-enabled digital twins. The system ingests heterogeneous data sources, detects and sanitizes PII, generates a statistically faithful synthetic digital twin using differential privacy, evaluates data quality according to DAMA-DMBOK dimensions, compresses the model context (-94.5%), and exposes native MCP tools for AI coding assistants.

The architecture comprises four decoupled operational stages and four concrete enterprise deliverables:

(A) Raw Data Ingestion & PII Sanitization

• Schema Sniffer: Delimiter and format auto-detection (CSV/TSV, Parquet/Arrow, SQLite, JSON, memory streams), strict type inference, encoding detection, and header validation.
• PII Sanitization: Pattern-based regex & heuristic interception for emails, phone numbers, tax IDs (DNI/NIE), credit cards, and API secrets/tokens before data enters the LLM prompt.

(B) Synthetic Digital Twin Generation

• Statistical Moment Profiler: Computes empirical moments ($\mu$, $\sigma$, min, max, skewness, kurtosis), categorical frequency distributions, and correlation structures.
• Differential Privacy Engine: Injects calibrated Laplacian/Gaussian noise $(\varepsilon, \delta)$ to generate statistically faithful mock datasets with identical column types and null dynamics without exposing a single real row.

(C) DAMA-DMBOK Quality Audit & Token Optimization

• DAMA-DMBOK 6 Dimensions: Rigorous audit of Completeness, Uniqueness, Validity, Consistency, Timeliness, and Accuracy.
• Tukey IQR Accuracy Engine: Evaluates outlier fences ($2.5 \times \text{IQR}$) and Z-score distributions across numeric domains.
• Token Compressor: Condenses tabular datasets into dense statistical fingerprints, achieving 90% to 95% prompt token reduction without loss of schema semantics.

(D) Model Context Protocol (MCP) & AI Integration

• FastMCP Server: Standardized JSON-RPC stdio protocol exposing 8 audit and transformation tools directly into Cursor IDE, Claude Desktop, Antigravity, and Cline.
• Enterprise Deliverables:
  1. Synthetic dataset (privacy-safe): Zero-leakage drop-in replacement for code generation and test execution.
  2. Quality audit report (DAMA): Multi-dimensional scorecards with radar diagrams and prioritized remediation steps.
  3. dbt schema (auto-generated): Production-ready schema.yml with not_null, unique, and value tests.
  4. Optimized context for LLMs: High-density token representations (e.g. 12,450 tokens $\rightarrow$ 680 tokens, -94.5% compression).

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Interactive HTML Dashboard

Generate zero-dependency, self-contained executive audit reports containing interactive SVG radar charts and dimension health gauges:

data-governance-mcp dashboard telemetry.csv --out audit_report.html

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Terminal CLI Experience

When working directly in your terminal, data-governance-mcp delivers a rich, color-coded diagnostic dashboard powered by rich:

data-governance-mcp audit examples/sample_datasets/industrial_furnace_telemetry.csv

┌─────────────────── DATA GOVERNANCE & PRIVACY TWIN AUDIT ────────────────────┐
│ Dataset: Delimited file (industrial_furnace_telemetry.csv)                  │
│ Records: 13  |  DAMA-DMBOK Score: 94.6 / 100 (EXCELLENT)                    │
│ Security & PII Risk: HIGH (2 findings flagged)                              │
└─────────────────────────────────────────────────────────────────────────────┘
                     DAMA-DMBOK 6 Core Quality Dimensions                      
┌──────────────┬────────┬────────┬─────────────┬──────────────────────────────┐
│ Dimension    │ Weight │  Score │   Status    │ Diagnostics                  │
├──────────────┼────────┼────────┼─────────────┼──────────────────────────────┤
│ Completeness │  22%   │  97.8% │  [ PASS ]   │ 2 null values detected (2.2% │
│              │        │        │             │ missingness).                │
│ Uniqueness   │  18%   │  84.6% │  [ FAIL ]   │ 1 exact duplicate row found. │
│ Validity     │  22%   │ 100.0% │  [ PASS ]   │ All columns conform to types.│
│ Accuracy     │  16%   │  89.5% │ [ WARNING ] │ 1 statistical outlier (IQR). │
│ Consistency  │  12%   │ 100.0% │  [ PASS ]   │ No logical contradictions.   │
│ Timeliness   │  10%   │  95.0% │  [ PASS ]   │ Evaluated on 'timestamp'.    │
└──────────────┴────────┴────────┴─────────────┴──────────────────────────────┘
┌──────────────────────────────── Action Plan ────────────────────────────────┐
│ Recommended Remediation Workflow:                                           │
│ 1. Generate privacy twin:  data-governance-mcp twin telemetry.csv           │
│ 2. Export dbt tests:       data-governance-mcp dbt telemetry.csv            │
│ 3. Generate HTML report:   data-governance-mcp dashboard telemetry.csv      │
└─────────────────────────────────────────────────────────────────────────────┘

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DAMA-DMBOK Quality Dimensions Matrix

Dimension	Weight	Detection Criteria	Remediation Strategy
Completeness	22%	Missing values (NaN, None) and whitespace string cells.	Targeted imputation or automated filtering.
Uniqueness	18%	Duplicate rows and natural primary key candidate viability.	Deduplication rules and surrogate key creation.
Validity	22%	Type conformance and mixed non-numeric values in numeric columns.	Robust schema casting and string sanitization.
Accuracy	16%	Tukey IQR fences (2.5x) and Z-score outlier detection.	Domain boundary enforcement and telemetry capping.
Consistency	12%	Cross-column logic (e.g. chronology inversions: end_date < start_date).	Relational sanity checks and constraint rules.
Timeliness	10%	Temporal freshness, date parsing validation, and cadence continuity.	ISO 8601 formatting and drift tracking.

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Quickstart

Installation

# Using uv (Recommended)
uv tool install data-governance-mcp

# Or via standard pip
pip install data-governance-mcp

Configure in Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "data-governance": {
      "command": "python",
      "args": ["-m", "data_governance_mcp.server"]
    }
  }
}

Configure in Cursor IDE / Antigravity / Cline

Add via standard stdio transport under Features > MCP Servers:

• Name: data-governance
• Type: command
• Command: python -m data_governance_mcp.server

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MCP Tools Reference

Tool Name	Parameters	Return Format	Purpose
audit_dataset	(file_path: str, max_rows: int = 50000)	Markdown	Full DAMA scorecard, PII risk level, and prioritized remediation plan.
generate_synthetic_twin	(file_path: str, output_csv_path: str, n_rows: int)	JSON	Anonymized statistical twin for safe vibe coding without data leakage.
compress_context_for_llm	(file_path: str, max_rows: int = 10000)	JSON	High-density statistical summary saving 90-95% of prompt tokens.
export_dbt_tests	(file_path: str, model_name: str)	YAML	Ready-to-commit schema.yml test suite for dbt projects.
export_html_dashboard	(file_path: str, output_html_path: str)	String (Path)	Standalone interactive HTML report with offline compatibility.
detect_pii	(file_path: str, max_sample_rows: int = 500)	JSON	Explicit pattern matching for emails, cards, phones, and API secrets.
evaluate_dama_dimensions	(file_path: str)	JSON	Granular dimension scores (0-100) and analytical diagnostics.
suggest_remediations	(file_path: str)	Markdown	Concrete Python and SQL scripts tailored to repair detected issues.

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Verification & Testing

Every release is verified across Python 3.10, 3.11, and 3.12:

pytest -v

tests/test_dimensions.py::test_completeness_perfect PASSED               [  5%]
tests/test_dimensions.py::test_completeness_with_nulls_and_blanks PASSED [ 10%]
tests/test_dimensions.py::test_uniqueness_with_duplicates PASSED         [ 15%]
tests/test_dimensions.py::test_accuracy_outliers PASSED                  [ 21%]
tests/test_dimensions.py::test_consistency_chronology_inversion PASSED   [ 26%]
tests/test_dimensions.py::test_evaluate_all_summary PASSED               [ 31%]
tests/test_loader.py::test_load_inline_csv PASSED                        [ 36%]
tests/test_loader.py::test_load_sqlite PASSED                            [ 42%]
tests/test_pii_detector.py::test_pii_detection_clean PASSED              [ 47%]
tests/test_pii_detector.py::test_pii_detection_email_and_secrets PASSED  [ 52%]
tests/test_server.py::test_audit_dataset_tool PASSED                     [ 57%]
tests/test_server.py::test_profile_schema_tool PASSED                    [ 63%]
tests/test_server.py::test_detect_pii_tool PASSED                        [ 68%]
tests/test_server.py::test_evaluate_dama_dimensions_tool PASSED          [ 73%]
tests/test_server.py::test_suggest_remediations_tool PASSED              [ 78%]
tests/test_wow_features.py::test_synthetic_twin_generator PASSED         [ 84%]
tests/test_wow_features.py::test_token_compressor PASSED                 [ 89%]
tests/test_wow_features.py::test_dbt_exporter PASSED                     [ 94%]
tests/test_wow_features.py::test_dashboard_exporter PASSED               [100%]

============================= 19 passed in 2.19s ==============================

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Author & Governance Credentials

Developed by Ioseba Alonso
Certified Data Management Professional (CDMP) by DAMA International & Industrial AI Practitioner.

• GitHub: @ioseba
• License: MIT