MCP.so
Sign In
O

Orionbelt Semantic Layer

@ralfbecher

Repository

About 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

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Frequently asked questions

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](https://docs.astral.sh/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.

Comments

Basic information

Category

Memory & Knowledge

Transports

stdio

Author

ralfbecher

Submitted by

Ralf Becher

More Memory & Knowledge MCP servers

Obsidian MCP Server

cyanheads

Read, write, search, and surgically edit Obsidian vault notes, tags, and frontmatter via MCP. STDIO or Streamable HTTP.

618

Anytype MCP Server

anyproto

An MCP server enabling AI assistants to interact with Anytype - your encrypted, local and collaborative wiki - to organize objects, lists, and more through natural language.

467

Basic Memory

basicmachines-co

AI conversations that actually remember. Never re-explain your project to your AI again. Join our Discord: https://discord.gg/tyvKNccgqN

3.4K

Context Portal MCP (ConPort)

GreatScottyMac

Context Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.

763

PLUR

plur-ai

AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference

226