rdf-mcp

uv run black .

uv run pytest

{
  "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())