gtfierro/rdf-mcp

This repository contains code for Model Context Protocol servers supporting use of the Brick and 223P ontologies.

Make sure you have uv installed.

This project uses Black for code formatting. To format your code, run:

uv run black .

Running tests

To run the test suite, use:

uv run pytest

This will discover and run all tests in the tests/ directory.

There are 2 MCP servers in this repository.

Brick MCP Server

Loads latest 1.4 Brick ontology from https://brickschema.org/schema/1.4/Brick.ttl

It defines these tools:

• expand_abbreviation: uses the Smash algorithm to attempt expanding common abbreviations (e.g. AHU) into Brick classes (e.g. Air_Handling_Unit)
• get_terms: returns a list of Brick classes
• get_properties: returns a list of Brick properties and object types
• get_possible_properties: returns a list of Brick properties and object types that can be used with a given Brick class
• get_definition_brick: returns the definition of a Brick class as the CBD of the Brick class

223P MCP Server

Loads latest 223P from https://open223.info/223p.ttl

• get_terms: returns a list of S223 classes
• get_properties: returns a list of S223 properties (not object types)
• get_possible_properties: returns a list of S223 properties and object types that can be used with a given S223 class
• get_definition_223p: returns the definition of a S223 class as the CBD of the S223 class

Running the servers

Claude Desktop

Should be as simple as uv run mcp install brick.py, then open Claude Desktop and look at the tools settings to ensure everything is working.

Open Claude Desktop and look at the tools settings to ensure everything is working.

Pydantic

import asyncio
from devtools import pprint
from pydantic_ai import Agent, capture_run_messages
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider
from pydantic_ai.mcp import MCPServerStdio

server = MCPServerStdio(
    "uv",
    args=[
        "run",
        "--with",
        "mcp[cli]",
        "--with",
        "rdflib",
        "--with",
        "oxrdflib",
        "mcp",
        "run",
        "/Users/gabe/src/rdf-mcp/rdf_mcp/servers/s223_server.py"
    ],
    env={
        "PYTHONPATH": "/Users/gabe/src/rdf-mcp"  # Update this path to your repo root
    },
)

model = OpenAIModel(
        model_name="gemma-3-27b-it-qat",
        # i'm using LM Studio here, but you could use any other provider that exposes
        # an OpenAI-like API
        provider=OpenAIProvider(base_url="http://localhost:1234/v1", api_key="lm_studio"),
    )

agent = Agent(
    model,
    mcp_servers=[server],
)

prompt = """Create a simple Brick model of a AHU box with 3 sensors: RAT, SAT and OAT. Also include a SF with a SF command

Look up definitions of concepts and their relationships to ensure you are building a valid Brick model.
Use the tool to determine what properties a term can have. Only use the predicates defined by the ontology.
Output a turtle file with the Brick model.
"""
async def main():
    with capture_run_messages() as messages:
        async with agent.run_mcp_servers():
            result = await agent.run(prompt)
    pprint(messages)
    print(result.output)

asyncio.run(main())