Model Context Protocol (MCP) is a standardized communication protocol that enables AI models to interact with external systems, tools, and resources in a consistent and secure manner. For AI operators, MCP serves as the universal interface layer that allows VLA models to access real-world data and execute actions across different platforms and environments.
Traditional AI models operate in isolation, limited to their training data and internal knowledge. MCP breaks down these barriers by providing:
- Universal Interface: One protocol for all external system interactions
- Consistent API: Predictable methods for different types of resources
- Cross-Platform Compatibility: Works across different operating systems and environments
- Live Information: Access to current data, not just training-time knowledge
- Dynamic Resources: Connect to databases, APIs, sensors, and live systems
- Contextual Awareness: Understanding of current system state and environment
- Permission Management: Granular control over what operators can access
- Audit Trails: Complete logging of all external interactions
- Sandboxed Execution: Safe operation boundaries for autonomous agents
MCP operates on a client-server model where AI operators act as clients requesting access to resources provided by MCP servers:
graph TB
A[AI Operator] --> B[MCP Client]
B --> C[MCP Server]
C --> D[File System]
C --> E[Database]
C --> F[APIs]
C --> G[Hardware Sensors]
C --> H[External Services]
B -.-> I[Authentication]
B -.-> J[Permission Checking]
B -.-> K[Request Validation]
MCP Client (AI Operator Side)
- Handles protocol communication
- Manages authentication and permissions
- Validates requests and responses
- Provides error handling and retry logic
MCP Server (Resource Side)
- Exposes resources through standardized interfaces
- Implements security and access controls
- Manages resource state and availability
- Provides resource discovery and documentation
Resource Types
- Tools: Executable functions and capabilities
- Resources: Data sources and information repositories
- Prompts: Template interactions and workflows
AI operators can discover available tools at runtime:
{
"method": "tools/list",
"result": {
"tools": [
{
"name": "read_file",
"description": "Read contents of a file",
"inputSchema": {
"type": "object",
"properties": {
"path": {"type": "string"}
}
}
},
{
"name": "execute_command",
"description": "Execute a system command",
"inputSchema": {
"type": "object",
"properties": {
"command": {"type": "string"},
"timeout": {"type": "number"}
}
}
}
]
}
}
Access to various types of resources:
{
"method": "resources/read",
"params": {
"uri": "file:///data/sensor_readings.json"
},
"result": {
"contents": [
{
"uri": "file:///data/sensor_readings.json",
"mimeType": "application/json",
"text": "{'temperature': 23.5, 'humidity': 65.2}"
}
]
}
}
Execute tools with proper error handling:
{
"method": "tools/call",
"params": {
"name": "move_robot_arm",
"arguments": {
"position": [10, 20, 30],
"speed": 0.5
}
},
"result": {
"content": [
{
"type": "text",
"text": "Robot arm moved to position [10, 20, 30] successfully"
}
]
}
}
Optum Protocol provides built-in MCP support for AI operators:
# optum-operator.yml
operator:
name: warehouse-robot
model: rt2-base
mcp:
servers:
- name: warehouse-system
uri: "mcp://warehouse.internal/api"
auth:
type: "api-key"
key: "${WAREHOUSE_API_KEY}"
tools:
- "move_item"
- "scan_barcode"
- "update_inventory"
- name: vision-system
uri: "mcp://vision.internal/api"
resources:
- "camera_feeds"
- "object_detection"
# AI Operator with MCP integration
class WarehouseOperator:
def __init__(self):
self.mcp_client = OptumMCPClient()
self.vla_model = load_vla_model("rt2-base")
async def process_instruction(self, instruction):
# Parse instruction using VLA model
action_plan = self.vla_model.plan(instruction)
# Execute actions using MCP tools
for action in action_plan:
if action.type == "move_item":
result = await self.mcp_client.call_tool(
"move_item",
{
"item_id": action.item_id,
"destination": action.destination
}
)
elif action.type == "scan":
result = await self.mcp_client.call_tool(
"scan_barcode",
{"location": action.location}
)
return result
MCP provides robust security mechanisms:
- API Keys: Simple key-based authentication
- OAuth 2.0: Standard web authentication flow
- Mutual TLS: Certificate-based authentication
- Custom Auth: Pluggable authentication providers
- Role-Based Access: Define roles with specific permissions
- Resource-Level Control: Granular permissions per resource
- Tool Restrictions: Limit which tools operators can use
- Rate Limiting: Prevent abuse and ensure fair resource usage
{
"timestamp": "2024-01-15T10:30:00Z",
"operator_id": "warehouse-robot-001",
"action": "tool_call",
"tool": "move_item",
"params": {"item_id": "SKU123", "destination": "shelf-A"},
"result": "success",
"duration_ms": 1250
}
// Reading configuration files
const config = await mcp.readResource("file:///config/robot-settings.json");
// Writing log files
await mcp.callTool("write_file", {
path: "/logs/operator.log",
content: "Task completed successfully"
});
// Query inventory database
const inventory = await mcp.callTool("query_database", {
query: "SELECT * FROM items WHERE location = ?",
params: ["warehouse-A"]
});
// Update item status
await mcp.callTool("update_database", {
table: "items",
id: "SKU123",
data: {"status": "moved", "location": "shelf-B"}
});
// Control robotic hardware
await mcp.callTool("move_robot", {
position: [x, y, z],
orientation: [rx, ry, rz],
speed: 0.5
});
// Read sensor data
const sensors = await mcp.readResource("sensors://environmental/current");
// Call external web APIs
const weather = await mcp.callTool("http_request", {
url: "https://api.weather.com/current",
method: "GET",
headers: {"Authorization": "Bearer token"}
});
// Send notifications
await mcp.callTool("send_notification", {
channel: "slack",
message: "Task completed",
urgency: "normal"
});
try:
result = await mcp_client.call_tool("move_item", params)
if result.error:
# Handle tool-specific errors
await handle_tool_error(result.error)
else:
# Process successful result
await process_success(result.content)
except MCPConnectionError:
# Handle connection issues
await reconnect_and_retry()
except MCPPermissionError:
# Handle permission issues
await request_elevated_permissions()
# Efficient resource usage
async with mcp_client.resource_context("camera_feed") as camera:
while task_active:
frame = await camera.get_frame()
analysis = vla_model.process_frame(frame)
if analysis.action_needed:
await execute_action(analysis.action)
# Cache frequently accessed resources
@mcp_cache(ttl=60) # Cache for 60 seconds
async def get_system_status():
return await mcp_client.read_resource("system://status")
# Batch operations when possible
results = await mcp_client.batch_call([
("tool1", params1),
("tool2", params2),
("tool3", params3)
])
The MCP protocol continues to evolve with new features:
- Streaming Protocols: Real-time data streaming for live sensors
- Federated Resources: Access resources across multiple organizations
- AI-Native Features: Built-in support for model-specific optimizations
- Enhanced Security: Advanced authentication and zero-trust architectures
MCP is essential for building production-ready AI operators that can interact safely and effectively with the real world through Optum Protocol.
