OrionBelt Analytics

Analyzes relational database schemas (PostgreSQL, Snowflake, and Dremio) and automatically generates comprehensive ontologies in RDF/Turtle format with direct SQL mappings.

The Ontology-based MCP server for your Text-2-SQL convenience.

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.

Run Analytics and Semantic Layer side-by-side in Claude Desktop for schema-aware ontology generationandguaranteed-correct SQL compilation.

- 8 database connectors-- PostgreSQL, MySQL, Snowflake, ClickHouse, Dremio, BigQuery, DuckDB/MotherDuck, Databricks SQL
- RDF/OWL ontology generationwithoba:namespace SQL annotations and W3C R2RML mappings
- GraphRAG-- graph traversal (up to 12 hops) + ChromaDB vector embeddings for semantic schema discovery
- SPARQL 1.1query 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_namesasks the host LLM to pre-fill rename suggestions for cryptic identifiers viasampling/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

A key differentiator of OrionBelt isOBQC(Ontology-Based Query Check), a deterministic, rule-based SQL validator that catches errorsbeforequeries 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.
- generate_ontologyorload_my_ontologycreates/loads an ontology withoba:namespace annotations that map OWL classes and properties to actual database tables, columns, types, and foreign keys.
- Whenexecute_sql_queryis called, OBQC parses the SQL withsqlglotand validates every table, column, join, and aggregation against the ontology's schema model.
- Issues are returned with severity levels (error, warning, info) alongside the query results, so the LLM can self-correct before the user sees wrong data.

OBQC is fully deterministic -- no LLM calls, no probabilistic reasoning. It acts as a safety net that complements the LLM's SQL generation with hard structural guarantees. Errorsblock query execution; warnings are attached to the response for the LLM to act on. SeeOBQC documentationfor the full rule reference, severity behavior, and annotation requirements.

git clone https://github.com/ralforion/orionbelt-analytics cd orionbelt-analytics uv sync

Seedocs/configuration.mdfor all environment variables, transport options, and troubleshooting.

{ "mcpServers": { "OrionBelt-Analytics": { "command": "npx", "args": [ "mcp-remote", "http://localhost:9000/mcp", "--transport", "http-only" ] } } }

claude mcp add orionbelt-analytics http://localhost:9000/mcp

SetMCP_TRANSPORT=ssein.env, restart the server, then add tolibrechat.yaml:

mcpServers: OrionBelt-Analytics: url: "http://host.docker.internal:9000/sse" timeout: 60000 startup: true

OrionBelt works with LangChain, OpenAI Agents SDK, CrewAI, Google ADK, Vercel AI SDK, n8n, and ChatGPT Custom GPTs. Seedocs/integrations.mdfor setup examples.

For full parameter details, return values, and examples, seedocs/tools-reference.md.

connect_database("postgresql") -> discover_schema("public") -> generate_ontology() -> execute_sql_query(...)

connect_database("duckdb") -> list_schemas() -> sample_table_data("events")

execute_sql_query(query) -> generate_chart(data, "bar", ...)

connect_database("postgresql") -> execute_sql_query(...)

Licensed under theBusiness Source License 1.1. The Licensed Work will convert to Apache License 2.0 on 2030-03-16.

By contributing to this project, you agree to theContributor License Agreement.

For commercial licensing inquiries, contact:licensing@ralforion.com

Expert-level PostgreSQL database analysis MCP server for AI assistants.