OrionBelt Analytics is an MCP server that analyzes relational database schemas and generates RDF/OWL ontologies with embedded SQL mappings. It provides relationship-aware Text-to-SQL with automatic fan-trap prevention, GraphRAG for intelligent schema discovery, and interactive charting -- all accessible through any MCP-compatible AI client.
The OrionBelt Ecosystem
| Project | Purpose |
|---|
| OrionBelt Analytics (this) | Schema analysis, ontology generation, GraphRAG, Text-to-SQL |
| OrionBelt Semantic Layer | Declarative YAML models compiled into dialect-specific, fan-trap-free SQL |
| OrionBelt Ontology Builder | Visual OWL ontology editor with reasoning and graph visualization (live demo) |
| OrionBelt Chat | AI chat UI for Analytics + Semantic Layer (Chainlit, multiple LLM providers) |
Run Analytics and Semantic Layer side-by-side in Claude Desktop for schema-aware ontology generation and guaranteed-correct SQL compilation.
Architecture
- 8 database connectors -- PostgreSQL, MySQL, Snowflake, ClickHouse, Dremio, BigQuery, DuckDB/MotherDuck, Databricks SQL
- RDF/OWL ontology generation with
oba: namespace SQL annotations and W3C R2RML mappings
- GraphRAG -- graph traversal (up to 12 hops) + ChromaDB vector embeddings for semantic schema discovery, run locally; a multilingual option lets questions in other languages find the schema (German "Umsatz" finds a column named "Net revenue")
- SPARQL 1.1 query interface via persistent Oxigraph RDF store
- OBQC validation -- deterministic SQL checks against the ontology (table/column existence, join validity, type mismatches, fan-traps)
- Interactive charting -- Plotly charts with MCP-UI rendering in Claude Desktop
- Multi-schema support -- analyze multiple schemas simultaneously; ontology and GraphRAG state are isolated per schema
- Workspace persistence -- reconnect to the same database and restore your previous session
- MCP sampling -- when the connected client supports sampling (e.g. OrionBelt Chat),
suggest_semantic_names asks the host LLM to pre-fill rename suggestions for cryptic identifiers via sampling/createMessage, collapsing the previous review-then-apply flow into a single tool call. Clients without sampling support (e.g. Claude Desktop) silently fall back to the manual review path
OBQC -- Ontology-Based Query Check
A key differentiator of OrionBelt is OBQC (Ontology-Based Query Check), a deterministic, rule-based SQL validator that catches errors before queries reach the database. Unlike LLM-only approaches that rely on the model "getting it right," OBQC cross-references every generated SQL statement against the loaded RDF/OWL ontology to enforce structural correctness.
What OBQC validates:
This is the beginning of the README. Read the rest at the original. Read the full README ↗
