OrionBelt Analytics

by ralfbecher

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About

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

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Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name OrionBelt Analytics

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "orionbelt analytics": {
            "server": {
                "command": "uvx",
                "args": [
                    "orionbelt-analytics"
                ]
            }
        }
    }
}

McpServers

{
    "server": {
        "command": "uvx",
        "args": [
            "orionbelt-analytics"
        ]
    }
}

Transport

"stdio"

Package

"orionbelt-analytics"

Registry

"pypi"

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 with sqlglotand 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. See OBQC documentationfor the full rule reference, severity behavior, and annotation requirements.

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

Edit.envwith 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.mdfor all environment variables, transport options, and troubleshooting.

The server starts onhttp://localhost:9000(HTTP transport, configurable viaMCP_SERVER_PORT).

Start the server, then add to yourclaude_desktop_config.json:

{ "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.

OrionBelt exposes 26 MCP tools. Here is a summary by category:

For full parameter details, return values, and examples, see docs/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", ...)

execute_sql_queryruns 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(...)

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]

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About

Explore

Setting up with Highlight

Claude Desktop / Cursor

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Transport

Package

Registry

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