orionbelt-analytics
Active·★ 53·NOASSERTION·Updated 2026-10-19
OrionBelt Analytics is an ontology-based MCP server designed to analyze relational database schemas, generate RDF/OWL ontologies, and facilitate relationship-aware Text-to-SQL queries. It integrates GraphRAG for intelligent schema discovery and offers robust SQL validation, including fan-trap prevention, ensuring accurate and secure data interactions.
#Text-to-SQL#Ontology#GraphRAG#Database Analytics#SQL Validation#LLM Tools#Data Connectors#Semantic Layer
01
Features
01Ontology-Based Query Check (OBQC) for deterministic SQL validation and fan-trap prevention.
02GraphRAG for intelligent semantic schema discovery and optimized context generation.
03Support for 8 major database connectors (PostgreSQL, MySQL, Snowflake, BigQuery, etc.).
04Relationship-aware Text-to-SQL with embedded SQL mappings via RDF/OWL ontologies.
05Interactive Plotly charting with MCP-UI rendering for data visualization.
02
Compatibility
Python
Runtime
Verified via docs
Docker
Deployment
Verified via docs
MCP
Protocol
Verified via docs
PostgreSQL
Database
Verified via docs
MySQL
Database
Verified via docs
Snowflake
Database
Verified via docs
03
Quick start
1
$ git clone https://github.com/ralforion/orionbelt-analytics
2
$ cd orionbelt-analytics
3
$ uv sync
04
Use cases
↳Conduct full analysis sessions from database connection to ontology generation and validated SQL querying.
↳Perform quick data exploration by connecting, listing schemas, and sampling table data.
↳Generate interactive Plotly charts directly from SQL query results for data visualization.
↳Enhance data understanding by generating and applying semantic names to cryptic schema identifiers.
↳Enable LLM-driven natural language interaction with databases through MCP-compatible AI clients.
05
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