Orionbelt Analytics

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OrionBelt® Analytics

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

[Version 2.2.2] [Python 3.13+] [License: BUSL-1.1] [FastMCP 4.0.5+] [RDF/OWL]

[BigQuery] [PostgreSQL] [Snowflake] [ClickHouse] [Dremio] [Databricks] [DuckDB] [MySQL]

[Docker Hub] [Docker pulls] [Image size]

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

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

Architecture

[OrionBelt Analytics 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:

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-analyticscd orionbelt-analyticsuv 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

Add to librechat.yaml:

mcpServers:  OrionBelt-Analytics:    type: streamable-http    url: "http://host.docker.internal:9000/mcp"    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

Ontology & Semantic

Query & Visualization

GraphRAG

SPARQL & RDF

Semantic Models

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(...)

Development

uv sync installs everything; uv run pytest, black/isort/ruff and strict mypy are the gates. The Development guide has the full setup, project layout, and contribution checklist.

One thing worth knowing before you open a workflow file: every GitHub Action is pinned to a 40-character commit SHA carrying a # vX.Y.Z comment, which is why they are full of hex. A git tag is a movable label, so actions/checkout@v7 runs whatever commit that label points at when the job starts; a SHA cannot move. The comments name exact patch releases rather than # v7, because a major tag moves with every upstream release. ./scripts/check-action-pins.sh resolves each tag upstream and fails when the commit it names is not the one pinned -- which is the only thing that distinguishes a real bump from a hash quietly swapped for one taken from a fork. It runs as the pins job on every pull request and as the first step of both publishing workflows; --offline skips the upstream lookups and checks only the SHA and comment format.

Documentation

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: [email protected]

Third-party software

OrionBelt Analytics builds on open source. THIRD_PARTY_NOTICES.md lists every bundled dependency with its licence, and calls out the few that carry obligations beyond attribution (psycopg2's LGPL, wordfreq's CC-BY-SA data, the MPL-2.0 components).

The Docker image redistributes those packages, so it ships their verbatim licence texts at /app/licenses/THIRD_PARTY_LICENSES.txt, alongside the Debian copyright files under /usr/share/doc/. The PyPI wheel bundles nothing third-party — it declares its dependencies and the installer fetches them from PyPI.

[RALFORION d.o.o.]

Copyright © 2026 RALFORION d.o.o.
OrionBelt® is a registered trademark of RALFORION d.o.o.