Orionbelt Semantic Layer

Name: Orionbelt Semantic Layer
Author: ralfbecher

@ralfbecher

关于 Orionbelt Semantic Layer

OrionBelt Semantic Layer is an API-first engine that transforms declarative YAML model definitions into optimized SQL for Postgres, Snowflake, ClickHouse, Dremio, and Databricks. It provides a unified abstraction over your data warehouse, so analysts and applications can query us

#semantic-layer #agentic-ai #semantic-sidecar #business-intelligence

基本信息

分类

记忆与知识

传输方式

stdio

发布者

ralfbecher

提交者

Ralf Becher

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "orionbelt": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://orionbelt.ralforion.com/mcp",
        "--transport",
        "http"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Orionbelt Semantic Layer?

OrionBelt Semantic Layer is an API-first engine that transforms declarative YAML semantic models into optimized SQL for Postgres, Snowflake, ClickHouse, Dremio, and Databricks. It provides a unified abstraction over data warehouses so analysts and applications can query using business concepts (dimensions, measures, metrics) instead of raw SQL.

How to use Orionbelt Semantic Layer?

Clone the repository, install dependencies with uv sync, then start the REST API with uv run orionbelt-api (available at http://127.0.0.1:8000) or the MCP server with uv run orionbelt-mcp. For Claude Desktop, add the server to claude_desktop_config.json. Optionally install the Gradio UI with uv sync --extra ui and access it at /ui.

Key features of Orionbelt Semantic Layer

5 SQL dialects: Postgres, Snowflake, ClickHouse, Dremio, Databricks
AST‑based SQL generation (no string concatenation)
YAML semantic models with dimensions, measures, metrics, and joins
Automatic join path resolution with Composite Fact Layer support
Vendor‑specific SQL validation via sqlglot (non‑blocking)
Precise error reporting with YAML source positions and join graph analysis
TTL‑scoped session management via REST API and MCP
ER diagram generation (Mermaid) via API and Gradio UI
9 MCP tools + 3 prompts for AI‑assisted model development
Gradio UI for interactive model editing and SQL compilation

Use cases of Orionbelt Semantic Layer

Compile business‑friendly queries (dimensions/measures) into dialect‑specific SQL
Integrate with AI assistants (Claude Desktop, Cursor) for semantic model authoring
Provide a unified semantic layer across multiple SQL databases
Validate and debug YAML model definitions with precise error messages
Generate ER diagrams from semantic models for documentation

FAQ from Orionbelt Semantic Layer

Which SQL dialects are supported?

Postgres, Snowflake, ClickHouse, Dremio, and Databricks SQL, each with dialect‑specific optimizations.

How do I run the MCP server?

Run uv run orionbelt-mcp for stdio mode (default, used with Claude Desktop) or set MCP_TRANSPORT=http for HTTP transport.

What tools and prompts does the MCP server expose?

9 tools: create_session, close_session, list_sessions, load_model, validate_model, describe_model, compile_query, list_models, list_dialects. 3 prompts: write_obml_model, write_query, debug_validation.

What are the prerequisites for installation?

Python 3.12+ and the uv package manager.

Is there a user interface besides the CLI and API?

Yes, a Gradio UI is available. Install with uv sync --extra ui and access it at /ui when the REST API server is running.

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