An automated data analysis pipeline built on the Model Context Protocol (MCP), enabling structured ETL workflows, statistical analysis, and automated insight generation. Designed for analysts who need repeatable, documented analytical processes.
Raw Data Sources → MCP Server → Transformation Layer → Analysis Engine → Insight Generation
The pipeline connects to multiple data sources (CSV, SQL databases, APIs), performs configurable transformations, runs statistical analyses, and produces structured insight reports with visualisations.
- Automated ETL: Connect to 10+ data source types with schema inference and validation
- Statistical Analysis: Descriptive statistics, hypothesis testing, correlation analysis, trend detection, anomaly identification
- Insight Generation: Natural language summaries of analytical findings with supporting evidence
- Pipeline Chaining: Compose multiple analysis steps into documented, reproducible workflows
- Export Flexibility: JSON, CSV, Markdown, or HTML report output
from data_analytics_mcp import AnalyticsPipeline
pipeline = AnalyticsPipeline(
sources=["data/sales_2025.csv"],
transforms=["clean_nulls", "normalize_dates", "detect_outliers"],
analyses=["trend_analysis", "segmentation", "correlation_matrix"]
)
report = pipeline.run()
report.export("output/analysis_report.html")| Use Case | Description | Typical Volume |
|---|---|---|
| Market Analysis | Competitor pricing trends, feature adoption rates | 10K+ records |
| User Behaviour | Session analysis, conversion funnel, retention cohorts | 50K+ events |
| Product Metrics | Feature usage, A/B test results, NPS trends | 5K+ responses |
| Operational | Efficiency metrics, cost analysis, resource allocation | 100K+ rows |
See /docs for pipeline configuration reference, connector setup guides, and example workflows.
MIT