embeddedlayers/mcp-analytics

v1.0.4 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 2mo MCP Data & Storage

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✓ No known CVEs patched

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✓ No known CVEs patched in this version

Topics

ai-agents ai-tools analytics anthropic business-intelligence claude

+14 more

csv-analysis cursor data-analytics data-science interactive-reports llm-tools machine-learning mcp mcp-server model-context-protocol no-code shopify-analytics statistics stripe-analytics

Summary

AI summary

Output datasets now save analysis results as reusable data for chaining analyses.

Full changelog

What's New

Community

Module Request template — request a custom analysis module directly from GitHub; our autonomous builder can deliver within days
Connector Request template — request new data source integrations
Public roadmap — see what's planned in ROADMAP.md
6 usage examples — CSV exploration, Shopify AOV, churn prediction, time series forecasting, A/B testing, linear regression. Each links to a live sample report.

Platform

Output datasets — analysis results saved as reusable datasets for chaining analyses
Dataset type separation — cleaner separation between uploaded inputs and generated outputs

Improved

R report pipeline simplified — card data now built in Python, removing external dependencies
Discovery accuracy improved with better LLM-generated module overviews
Error messages now suggest specific corrective actions with column name hints

Full changelog: CHANGELOG.md

Try it: Sign up free at app.mcpanalytics.ai — 2,000 credits, no credit card required.

What should we build next? Vote on connectors · Request a module · View roadmap

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About embeddedlayers/mcp-analytics

Statistical analysis, forecasting, and ML for business data (Shopify, Stripe, WooCommerce, eBay, GA4, Search Console). Upload a CSV or connect live data sources — ask a question in Claude or Cursor, get an interactive HTML report.

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