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
• 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:

Check	What it catches
Table existence	References to tables that don't exist in the schema
Column existence	References to columns not present in their table, ambiguous unqualified columns
Join validity	Missing join conditions (Cartesian products), join columns that don't match declared foreign keys
Type compatibility	WHERE/ON comparisons between incompatible types (e.g. string vs. integer)
Aggregation correctness	SELECT columns missing from GROUP BY when aggregates are used
Fan-trap detection	Aggregations across multiple one-to-many joins that silently multiply results

How it works:

1. generate_ontology or load_my_ontology creates/loads an ontology with oba: namespace annotations that map OWL classes and properties to actual database tables, columns, types, and foreign keys.
2. When execute_sql_query is called, OBQC parses the SQL with sqlglot and validates every table, column, join, and aggregation against the ontology's schema model.
3. 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. Errors block query execution; warnings are attached to the response for the LLM to act on. See OBQC documentation for the full rule reference, severity behavior, and annotation requirements.

Quick Start

1. Install

git clone https://github.com/ralforion/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 26 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 OBQC 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
reachable_from	Dimension-capable tables for an anchor grain (many-to-one closure)
measurable_from	Measure-capable tables for an anchor grain (one-to-many closure)
plan_composite_query	Advise a fan-trap-safe Composite Fact Layer (UNION ALL) decomposition

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

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:

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

execute_sql_query runs OBQC validation, security checks, and fan-trap detection before executing — no separate validation step is needed.

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
OBQC	Validation rules, severity levels, blocking behavior, annotation requirements
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. The Licensed Work will convert to Apache License 2.0 on 2030-03-16.

By contributing to this project, you agree to the Contributor License Agreement.

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

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