brick-ontology-mcp

An MCP server that gives LLMs native access to the Brick Schema ontology — the open standard for describing building metadata (equipment, sensors, locations, and relationships).

Validate, search, and explore 1000+ Brick classes without leaving your AI coding assistant. Fully offline after install.

Why

If you work with smart building data, you've hit these problems:

1. Assigning classes that don't exist — RDF silently accepts brick:Chilled_Water_Thingy without complaint

2. Not knowing what's available — with 1000+ classes, it's hard to find the right one

3. Reinventing existing classes — creating My_Custom_Temp_Sensor when Zone_Air_Temperature_Sensor already exists

4. Wrong specificity level — using brick:Sensor when a more precise subclass is available

This MCP server solves all four by making any MCP-compatible client (Claude Code, Claude Desktop, Cursor, etc.) aware of the full Brick class hierarchy.

Related MCP server: rdf-mcp

Tools

All tools are read-only and fully offline — the Brick ontology is bundled with the brickschema Python package. No API keys, no network calls.

Installation

From source

git clone https://github.com/ucl-sbde/brick-ontology-mcp.git
cd brick-ontology-mcp
pip install .

Or with uv:

uv pip install .

Configuration

Claude Code

Add to your project's .mcp.json:

{
  "mcpServers": {
    "brick-ontology": {
      "command": "brick-ontology-mcp"
    }
  }
}

Or globally in ~/.claude.json:

{
  "mcpServers": {
    "brick-ontology": {
      "command": "brick-ontology-mcp"
    }
  }
}

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "brick-ontology": {
      "command": "brick-ontology-mcp"
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project:

{
  "mcpServers": {
    "brick-ontology": {
      "command": "brick-ontology-mcp"
    }
  }
}

Example Usage

"Does this class exist?"

You: Assign brick:Chilled_Water_Thingy to this valve

The LLM calls brick_validate_class("Chilled_Water_Thingy") and gets:

{
  "exists": false,
  "normalized_to": "Chilled_Water_Thingy",
  "suggestions": [
    {"class_name": "Chilled_Water_Valve", "similarity": 0.8},
    {"class_name": "Chilled_Water_Pump", "similarity": 0.65}
  ]
}

"What classes exist for temperature sensors?"

You: What types of temperature sensors does Brick have?

The LLM calls brick_search_classes("temperature sensor") and gets all matching classes with their categories and parent classes.

"Am I reinventing the wheel?"

You: I'll create a custom Hot_Water_Supply_Temp class

The LLM calls brick_search_classes("hot water temperature") and discovers Hot_Water_Supply_Temperature_Sensor already exists.

"What's the hierarchy?"

You: Where does Zone_Air_Temperature_Sensor sit in the ontology?

The LLM calls brick_get_hierarchy("Zone_Air_Temperature_Sensor", direction="ancestors") and gets:

Zone_Air_Temperature_Sensor
  -> Air_Temperature_Sensor
    -> Temperature_Sensor
      -> Sensor
        -> Point

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Test the server with MCP Inspector
npx @modelcontextprotocol/inspector brick-ontology-mcp

How It Works

The server loads the Brick Schema ontology (v1.4+) at startup using the brickschema Python library. It pre-indexes all class names, parent/child relationships, and category assignments into in-memory data structures. Tool calls are sub-millisecond lookups against this index — no SPARQL queries at runtime for validation and search.

Built with FastMCP (the official MCP Python SDK).

License

MIT