Orionbelt Analytics
Ontology-based MCP server that analyzes database schemas (PostgreSQL, Snowflake, ClickHouse, Dremio) and generates RDF/OWL ontologies with SQL mappings for fan-trap-free Text-to-SQL.
What is Orionbelt Analytics?
Orionbelt Analytics is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to ontology-based mcp server that analyzes database schemas (postgresql, snowflake, clickhouse, dremio) and generates rdf/owl ontologies with sql mappings for fan-trap-free text-to-sql.
Ontology-based MCP server that analyzes database schemas (PostgreSQL, Snowflake, ClickHouse, Dremio) and generates RDF/OWL ontologies with SQL mappings for fan-trap-free Text-to-SQL.
This server falls under the Analytics category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Ontology-based MCP server that analyzes database schemas (Po
Use Cases
Maintainer
Works with
Installation
PIP
pip install orionbelt-analyticsManual Installation
pip install orionbelt-analyticsConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Orionbelt Analytics
OrionBelt Analytics is an ontology-based MCP server that connects to relational databases — PostgreSQL, MySQL, Snowflake, ClickHouse, Dremio, BigQuery, DuckDB/MotherDuck, and Databricks SQL — analyzes their schemas, and generates RDF/OWL ontologies with embedded SQL mappings to enable accurate, fan-trap-free Text-to-SQL. Its OBQC (Ontology-Based Query Check) layer deterministically validates every generated SQL statement against the ontology before execution, catching table/column errors, invalid joins, aggregation mistakes, and fan-trap conditions that LLMs alone would silently pass. Data analysts and BI teams use it to get reliable natural-language SQL generation over complex multi-table schemas without getting incorrect numbers from fan-trap joins.
Prerequisites
- Python 3.13 or later installed
- uv package manager installed (curl -LsSf https://astral.sh/uv/install.sh | sh)
- Access credentials for at least one supported database (PostgreSQL, Snowflake, ClickHouse, BigQuery, etc.)
- An MCP-compatible client such as Claude Desktop or Claude Code
- Optional: ChromaDB and Oxigraph are bundled; Qdrant is not required for core functionality
Clone the repository and install dependencies
Clone the OrionBelt Analytics repository and use uv to sync all Python dependencies. Python 3.13+ is required.
git clone https://github.com/ralfbecher/orionbelt-analytics
cd orionbelt-analytics
uv syncCreate and configure the .env file
Copy the environment template and edit it with your database credentials. Set at minimum the variables for one database backend (for example POSTGRES_HOST, POSTGRES_PORT, POSTGRES_DATABASE, POSTGRES_USERNAME, POSTGRES_PASSWORD).
cp .env.template .envStart the OrionBelt Analytics server
The server starts on HTTP transport at localhost:9000 by default. The port is configurable via MCP_SERVER_PORT in .env.
uv run server.pyConnect Claude Desktop via mcp-remote
Because OrionBelt uses HTTP transport, Claude Desktop connects via the mcp-remote proxy. Add the following to your claude_desktop_config.json.
Connect and discover your schema
Once connected in Claude Desktop, use the connect_database tool to establish a connection, then discover_schema to analyze the schema, build the GraphRAG index, and generate the initial ontology.
Generate the RDF/OWL ontology
After discovery, call generate_ontology to produce a fully annotated Turtle (.ttl) file with oba: namespace SQL mappings. This ontology powers OBQC validation for all subsequent SQL generation.
Orionbelt Analytics Examples
Client configuration
OrionBelt Analytics uses HTTP transport. Connect Claude Desktop via mcp-remote. Start the server first, then add this to claude_desktop_config.json.
{
"mcpServers": {
"OrionBelt-Analytics": {
"command": "npx",
"args": [
"mcp-remote",
"http://localhost:9000/mcp",
"--transport",
"http-only"
]
}
}
}Prompts to try
These prompts walk through the typical workflow: connect to a database, discover its schema, generate an ontology, and then run validated Text-to-SQL queries.
- "Connect to my PostgreSQL database and discover the schema for the 'sales' schema"
- "Generate an RDF/OWL ontology from the connected schema"
- "How many orders were placed per customer last month? Generate and run the SQL"
- "Find all join paths between the orders and products tables"
- "Generate a bar chart of monthly revenue for 2024 using the orders table"Troubleshooting Orionbelt Analytics
connect_database fails with connection timeout or authentication error
Verify all database credentials in .env match exactly (host, port, database name, username, password). For cloud databases like Snowflake or BigQuery, also check that the account identifier and warehouse/project settings are correct.
execute_sql_query is blocked by OBQC with 'fan-trap detected' error
This is OBQC working correctly — the generated SQL has a join across multiple one-to-many relationships that would multiply results. Rephrase the question to compute aggregations separately per relationship or ask OrionBelt to rewrite the query using subqueries.
Claude Desktop cannot reach the server with 'connection refused' on localhost:9000
Ensure the server is running (uv run server.py) before starting Claude Desktop. If the port is in use, set MCP_SERVER_PORT to a free port in .env and update the mcp-remote URL in claude_desktop_config.json accordingly.
Frequently Asked Questions about Orionbelt Analytics
What is Orionbelt Analytics?
Orionbelt Analytics is a Model Context Protocol (MCP) server that ontology-based mcp server that analyzes database schemas (postgresql, snowflake, clickhouse, dremio) and generates rdf/owl ontologies with sql mappings for fan-trap-free text-to-sql. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Orionbelt Analytics?
Install via pip with: pip install orionbelt-analytics. Then configure your AI client to connect to this MCP server.
Which AI clients work with Orionbelt Analytics?
Orionbelt Analytics works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Orionbelt Analytics free to use?
Yes, Orionbelt Analytics is open source and available under the NOASSERTION license. You can use it freely in both personal and commercial projects.
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Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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