# Simple NVIDIA NeMo Agent Toolkit Configuration for Volume Canvas MCP Server
# This configuration uses only built-in NAT tools to create a working MCP server
# Define the workflow - using a simple react_agent
workflow:
_type: react_agent
name: "volume_canvas_workflow"
description: "Hammerspace Volume Canvas operations workflow"
verbose: true
parse_agent_response_max_retries: 3
# Define tools using only built-in NAT function types
functions:
# Use built-in tools that are available
current_datetime:
_type: current_datetime
description: "Get current date and time"
# Use code execution tool for custom volume canvas operations
volume_canvas_operations:
_type: code_execution
description: "Execute Volume Canvas operations (list volumes, files, etc.)"
code_template: |
# Volume Canvas Operations
import json
import os
from datetime import datetime
# Mock data for demonstration
volumes = [
{"uuid": "vol-001", "name": "hot-storage", "type": "hot", "size_gb": 1000},
{"uuid": "vol-002", "name": "warm-storage", "type": "warm", "size_gb": 2000},
{"uuid": "vol-003", "name": "cold-storage", "type": "cold", "size_gb": 5000}
]
files = [
{"name": "model1.pt", "path": "/models/model1.pt", "size_mb": 500},
{"name": "dataset1.csv", "path": "/data/dataset1.csv", "size_mb": 100}
]
operation = "{{operation}}"
if operation == "list_volumes":
result = {"volumes": volumes, "timestamp": datetime.now().isoformat()}
elif operation == "list_files":
result = {"files": files, "timestamp": datetime.now().isoformat()}
else:
result = {"message": f"Operation '{operation}' completed", "timestamp": datetime.now().isoformat()}
print(json.dumps(result, indent=2))
# LLM configuration for the agent
llms:
nim_llm:
_type: nim
model_name: "meta/llama-3.1-70b-instruct"
temperature: 0.0
nvidia_api_key: "${NVIDIA_API_KEY}"
