API-first semantic engine and query planner for AI agents that compiles declarative YAML models into optimized, dialect-specific SQL across BigQuery, PostgreSQL, Snowflake, ClickHouse, Dremio, Databricks, DuckDB, and MySQL.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"orionbelt": {
"args": [
"orionbelt-semantic-layer-mcp"
],
"command": "uvx"
}
}
}Are you the author?
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OrionBelt Semantic Layer is an API-first semantic engine and query planner for AI agents that compiles and executes declarative YAML model definitions as optimized SQL for BigQuery, ClickHouse, Databricks, Dremio, DuckDB/MotherDuck, MySQL, Postgres, and Snowflake. Query using business concepts — dimensions, measures, and metrics — instead of raw SQL.
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Checked orionbelt-semantic-layer against OSV.dev.
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Compile and execute YAML semantic models as analytical SQL across multiple database dialects
OrionBelt Semantic Layer is an API-first semantic engine and query planner for AI agents that compiles and executes declarative YAML model definitions as optimized SQL for BigQuery, ClickHouse, Databricks, Dremio, DuckDB/MotherDuck, MySQL, Postgres, and Snowflake. Query using business concepts — dimensions, measures, and metrics — instead of raw SQL.
Analytics as Code — Define your analytical semantics in version-controlled YAML, compile to dialect-specific SQL, and execute against live databases, all through a single API. No BI tool in the middle: the full loop from declarative model to query results is programmable, reviewable, and reproducible.
Companion Project: OrionBelt Analytics — an ontology-based MCP server that analyzes database schemas and generates RDF/OWL ontologies. Together they let AI assistants navigate your data landscape through ontologies and compile safe, dialect-aware analytical SQL.