OrionBelt Analytics

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.

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
• SPARQL 1.1 query interface via persistent Oxigraph RDF store
• Fan-trap prevention -- automatic detection and safe query pattern suggestions
• 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

Quick Start

1. Install

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

Requires Python 3.13+ and uv.

2. Configure

cp .env.template .env

Edit .env with your database credentials. At minimum, set the variables for one database (e.g. POSTGRES_HOST, POSTGRES_PORT, POSTGRES_DATABASE, POSTGRES_USERNAME, POSTGRES_PASSWORD).

See docs/configuration.md for all environment variables, transport options, and troubleshooting.

3. Run

uv run server.py

The server starts on http://localhost:9000 (HTTP transport, configurable via MCP_SERVER_PORT).

Connect Your AI Client

Claude Desktop

Start the server, then add to your claude_desktop_config.json:

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

Claude Code

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

LibreChat

Set MCP_TRANSPORT=sse in .env, restart the server, then add to librechat.yaml:

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

Other Frameworks

OrionBelt works with LangChain, OpenAI Agents SDK, CrewAI, Google ADK, Vercel AI SDK, n8n, and ChatGPT Custom GPTs. See docs/integrations.md for setup examples.

Tools

OrionBelt exposes 32 MCP tools. Here is a summary by category:

Connection & Schema

Tool	Description
connect_database	Connect to any supported database using .env credentials
list_schemas	List available schemas in the connected database
reset_cache	Clear cached schema and ontology data for the current session
discover_schema	Analyze schema structure with automatic GraphRAG + ontology generation
get_table_details	Get detailed column, key, and constraint info for a specific table
cleanup_workspace	Delete all workspace files for the current connection and start fresh

Ontology & Semantic

Tool	Description
generate_ontology	Generate RDF/OWL ontology from schema with SQL mapping annotations
suggest_semantic_names	Detect abbreviations and cryptic names for business-friendly renaming
apply_semantic_names	Apply LLM-suggested semantic names and descriptions to ontology
load_my_ontology	Load a custom .ttl ontology file from an import folder
download_artifact	Download ontology or R2RML mapping as a Turtle file

Query & Visualization

Tool	Description
sample_table_data	Preview table data with row limit and injection protection
execute_sql_query	Execute SQL with built-in validation, security checks, and fan-trap detection
generate_chart	Generate Plotly charts (bar, line, scatter, heatmap) with MCP-UI rendering

GraphRAG

Tool	Description
graphrag_search	Semantic search + schema overview (auto-initialized by discover_schema)
graphrag_query_context	Get optimized context for SQL generation (85-95% token reduction)
graphrag_find_join_path	Discover join paths between tables via graph traversal

SPARQL & RDF

Tool	Description
store_ontology_in_rdf	Persist ontology in Oxigraph for SPARQL access
query_sparql	Execute SPARQL queries (SELECT, ASK, CONSTRUCT — auto-detected)
add_rdf_knowledge	Add custom metadata triples to the RDF store

Semantic Models

Tool	Description
save_semantic_model	Save a semantic model (e.g., OBML YAML) to the workspace
get_semantic_model	Retrieve a stored semantic model by name
list_semantic_models	List all stored semantic models for the current connection

System

Tool	Description
get_server_info	Server version, features, and configuration

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

Typical Workflows

Full analysis session:

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

Quick data exploration:

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

Query with visualization:

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

Resume a previous session (auto-restores workspace):

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

Documentation

Document	Contents
Tools Reference	Full parameter docs, return values, and usage examples
Configuration	Environment variables, transport setup, troubleshooting
GraphRAG	Graph-based schema intelligence and OBML workflow
Fan-Trap Prevention	The fan-trap problem, detection, and safe SQL patterns
Integrations	LangChain, OpenAI, CrewAI, Google ADK, Vercel, n8n, ChatGPT
Development	Project structure, testing, contributing

License

Copyright 2025-2026 RALFORION d.o.o.

Licensed under the Business Source License 1.1. See LICENSE for details.

Change Date: 2030-03-16 | Change License: Apache License, Version 2.0

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

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