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 .To run the test suite, use:
uv run pytestThis will discover and run all tests in the tests/ directory.
There are 2 MCP servers in this repository.
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 classesget_properties: returns a list of Brick properties and object typesget_possible_properties: returns a list of Brick properties and object types that can be used with a given Brick classget_definition_brick: returns the definition of a Brick class as the CBD of the Brick class
Loads latest 223P from https://open223.info/223p.ttl
get_terms: returns a list of S223 classesget_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 classget_definition_223p: returns the definition of a S223 class as the CBD of the S223 class
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.
I had to make some edits for these to work on my own Claude Desktop installation. Note: You must set the PYTHONPATH environment variable to the root of this repository so that the servers can import the rdf_mcp package. Here is what my claude_desktop_config.json file looks like (update the paths as needed for your system):
{
"mcpServers": {
"BrickOntology": {
"command": "/Users/gabe/.cargo/bin/uv",
"args": [
"run",
"--with",
"mcp[cli]",
"--with",
"rdflib",
"--with",
"oxrdflib",
"mcp",
"run",
"/Users/gabe/src/rdf-mcp/rdf_mcp/servers/brick_server.py"
],
"env": {
"PYTHONPATH": "/Users/gabe/src/rdf-mcp"
}
},
"S223Ontology": {
"command": "/Users/gabe/.cargo/bin/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"
}
}
}
}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())