Macros

#[mcp]
QUERY QueryName(param1: Type, param2: Type) =>
    result <- traversal_expression
    RETURN result

N::User {
    name: String,
    age: U32,
    email: String
}

#[mcp]
QUERY get_user(user_id: ID) =>
    user <- N<User>(user_id)
    RETURN user

# Initialize the connection, this will return a connection_id
curl -X POST \
  http://localhost:6969/mcp/init \    
  -H 'Content-Type: application/json' \
  -d '{}'

# Use the connection_id from above to call the MCP endpoint
curl -X POST \
  http://localhost:6969/mcp/get_userMcp \
  -H 'Content-Type: application/json' \
  -d '{
    "connection_id": "connection_id",
    "data": {
        "user_id": "user_id"
      }
    }'

from helix.client import Client
from helix.mcp import MCPServer
from helix.providers.openai_client import OpenAIProvider
from fastmcp.tools.tool import FunctionTool

# Initialize MCP server
client = Client(local=True)
mcp = MCPServer("helix-mcp", client)

# Add your custom tool to the MCP server
def get_user(connection_id: str, user_id: str):
    """
    Get the name of a user by their ID
    Args: connection_id: The connection ID, user_id: The ID of the user
    Returns: The user object
    """
    return client.query(
        "mcp/get_userMcp", 
        {"connection_id": connection_id, "data":{"user_id": user_id}}
    )

mcp.add_tool(FunctionTool.from_function(get_user))

# Start MCP server
mcp.run_bg()

# Enable MCP in your LLM provider
llm = OpenAIProvider(
    name="openai-llm",
    instructions="You are a helpful assistant with access to user data.",
    model="gpt-4o",
    history=True
)
llm.enable_mcps("helix-mcp")

# The AI can now call your MCP queries
response = llm.generate(f"What is the name of user with ID {user_id}?")
print(response)