orionbelt-analytics

<!-- mcp-name: io.github.ralforion/orionbelt-analytics --> <p align="center"> <img src="https://raw.githubusercontent.com/ralforion/orionbelt-analytics/main/assets/ORIONBELT_Logo.png" alt="OrionBelt Logo" width="400"> </p> <h1 align="center">OrionBelt® Analytics</h1> <p align="center"><strong>The Ontology-based MCP server for your Text-2-SQL convenience.</strong></p>

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

<p align="center"> <img src="https://raw.githubusercontent.com/ralforion/orionbelt-analytics/main/assets/architecture.png" alt="OrionBelt Analytics Architecture" width="900"> </p>

• 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