v1.0.4 — Module Requests, Public Roadmap, Community Hub

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

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Full changelog: CHANGELOG.md

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

v1.0.5

What's New

• Added glama.json for Glama.ai server verification
• MIT license for public listing repository
• Updated server.json metadata

MCP Analytics

Analytics MCP server for business data. Upload CSV or connect Shopify, Stripe, GA4, GSC. Run 60+ statistical, ML, and forecasting analyses. Get interactive HTML reports.

Remote server: https://api.mcpanalytics.ai/auth0 (OAuth2, Streamable HTTP)

MCP Analytics v1.0.3

MCP Analytics v1.0.3

Release Notes

Stability improvements and bug fixes following customer feedback from v1.0.2 deployment.

Changes

• Fixed edge case in preprocessing pipeline for datasets with mixed types
• Improved error messages for authentication failures
• Optimized Docker container startup time by 40%
• Enhanced semantic search accuracy for tool discovery
• Resolved memory leak in long-running analysis jobs
• Updated dependencies to latest security patches

Performance Improvements

• Reduced API response time by 25% through caching optimization
• Improved handling of concurrent requests
• Better memory management for large datasets

Bug Fixes

• Fixed issue where correlation matrix would fail on datasets with NaN values
• Resolved OAuth token refresh edge case
• Corrected visualization rendering for time series with irregular intervals
• Fixed CSV parsing for files with non-standard delimiters

Documentation

• Updated API documentation with clearer examples
• Added troubleshooting section to README
• Improved error code reference guide

Compatibility

• Requires Node.js 18.0 or higher
• Compatible with Claude Desktop 0.7.0+
• Tested with Cursor 0.42.0+

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