DataLens MCP-Powered Database Insight Engine
Link to open source: https://github.com/darshan-sharma4/DataLens
🔹 Project Description
DataLens is an AI-driven database intelligence platform that transforms raw relational databases into structured, human-readable insights.
It automatically analyzes database schemas to detect:
Primary keys
Foreign keys
Table relationships
Data quality metrics
Using Gemini API, the system generates:
Natural-language schema explanations
AI-powered table descriptions
Query interpretations
Intelligent database insights
The platform enables developers and data teams to:
Understand complex SQL databases instantly
Visualize relationships between tables
Detect structural issues and inconsistencies
Improve query design and database architecture
DataLens integrates an intelligent inspection layer (MCP-style architecture) that allows AI to:
Analyze schema metadata
Interpret SQL queries
Suggest optimizations
Provide contextual explanations of database relationships
Designed for scalability, the system supports PostgreSQL and can be extended to multiple SQL engines.
🎯 Core Goal
To bridge the gap between complex relational databases and human understanding using AI-powered schema intelligence.
💡 Problem It Solves
Modern databases are powerful but difficult to interpret without documentation.
DataLens eliminates manual schema exploration by using AI to automatically interpret and explain database structures.
🌟 Innovation Angle (Google-Level Framing)
Combines structured database introspection with LLM-powered reasoning
Uses Gemini for contextual schema understanding
Provides developer-centric AI tooling for data engineering workflows
Turns databases into self-explaining systems
This build was uploaded as a hackathon project
