Register Genesys Cloud LLM Gateway Tool Definitions via Python API

What You Will Build

• A Python service that constructs, validates, and registers custom LLM tool definitions with the Genesys Cloud AI Gateway.
• The implementation uses the /api/v2/ai/assistant/tools endpoint and the genesys-cloud-python-sdk for credential management.
• The tutorial covers Python 3.10+ with httpx, jsonschema, tenacity, and opentelemetry.

Prerequisites

• OAuth client credentials with ai:tool:write and ai:tool:read scopes
• genesys-cloud-python-sdk version 2.0.0+
• Python 3.10 or higher
• External dependencies: httpx, pydantic, jsonschema, tenacity, opentelemetry-api, opentelemetry-sdk, fastapi, uvicorn

Authentication Setup

The Genesys Cloud API requires OAuth 2.0 client credentials flow. The official Python SDK handles token acquisition and refresh automatically when initialized with environment variables. The following configuration establishes the platform client and verifies scope availability.

import os
import logging
from genesyscloud.platform.client import PlatformClientBuilder
from genesyscloud.ai.api import AiToolsApi

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

def initialize_genesys_client() -> AiToolsApi:
    """
    Initializes the Genesys Cloud SDK client with OAuth credentials.
    Raises ValueError if required environment variables are missing.
    """
    required_vars = ["GENESYS_CLOUD_REGION", "GENESYS_CLOUD_CLIENT_ID", "GENESYS_CLOUD_CLIENT_SECRET"]
    missing = [v for v in required_vars if not os.getenv(v)]
    if missing:
        raise ValueError(f"Missing required environment variables: {missing}")

    config = PlatformClientBuilder(
        environment=os.getenv("GENESYS_CLOUD_REGION"),
        client_id=os.getenv("GENESYS_CLOUD_CLIENT_ID"),
        client_secret=os.getenv("GENESYS_CLOUD_CLIENT_SECRET")
    ).build()

    # Verify OAuth scopes are correctly assigned to the client
    client = config.get_default_api_client()
    token_info = client.get_token()
    if "ai:tool:write" not in token_info.get("scope", ""):
        raise PermissionError("OAuth client lacks required scope: ai:tool:write")

    return config.get_ai_tools_api()

The SDK caches the access token and automatically requests a refresh token when the current token expires. You do not need to implement manual token rotation logic.

Implementation

Step 1: Construct and Validate Tool Schema

The LLM gateway expects a JSON Schema draft 2020-12 compliant definition. The gateway rejects schemas containing unsupported types or exceeding parameter limits. The following function constructs the payload and validates it against both the JSON Schema specification and a capability matrix that prevents hallucination loops by enforcing strict type boundaries.

import jsonschema
import json
from typing import Dict, Any, List

GENESYS_CAPABILITY_MATRIX = {
    "max_parameters": 10,
    "allowed_types": ["string", "number", "integer", "boolean", "array", "object"],
    "max_description_length": 255
}

def build_tool_schema(tool_name: str, description: str, parameters: Dict[str, Any], execution_url: str) -> Dict[str, Any]:
    """
    Constructs and validates a Genesys Cloud LLM tool definition.
    """
    schema_properties: Dict[str, Any] = {}
    required_params: List[str] = []

    for param_name, param_def in parameters.items():
        param_type = param_def.get("type", "string")
        if param_type not in GENESYS_CAPABILITY_MATRIX["allowed_types"]:
            raise ValueError(f"Unsupported type '{param_type}' for parameter '{param_name}'. Allowed types: {GENESYS_CAPABILITY_MATRIX['allowed_types']}")
        
        schema_properties[param_name] = {
            "type": param_type,
            "description": param_def.get("description", "")
        }
        if param_def.get("required", False):
            required_params.append(param_name)

    if len(schema_properties) > GENESYS_CAPABILITY_MATRIX["max_parameters"]:
        raise ValueError(f"Tool exceeds maximum parameter limit of {GENESYS_CAPABILITY_MATRIX['max_parameters']}")

    tool_payload = {
        "name": tool_name,
        "description": description,
        "schema": {
            "type": "object",
            "properties": schema_properties,
            "required": required_params,
            "$schema": "https://json-schema.org/draft/2020-12/schema"
        },
        "executionEndpoint": execution_url,
        "rateLimit": {
            "requestsPerMinute": 60,
            "burstSize": 10
        }
    }

    # Validate against JSON Schema draft 2020-12
    jsonschema.Draft202012Validator.check_schema(tool_payload["schema"])
    
    logger.info("Tool schema validated successfully: %s", tool_name)
    return tool_payload

Step 2: Register Tool via API with Retry Logic

The registration endpoint supports pagination when listing tools and returns specific error codes for validation failures. The following code demonstrates the full HTTP cycle, implements exponential backoff for rate limits, and handles authentication and authorization errors explicitly.

import httpx
import time
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
from typing import Optional

GENESYS_API_BASE = "https://api.{region}.mypurecloud.com"

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=2, max=10),
    retry=retry_if_exception_type(httpx.HTTPStatusError),
    reraise=True
)
def register_tool(api_client: AiToolsApi, tool_payload: Dict[str, Any]) -> Dict[str, Any]:
    """
    Registers a tool definition via the Genesys Cloud AI Tools API.
    Includes full HTTP cycle logging and explicit error handling.
    """
    region = os.getenv("GENESYS_CLOUD_REGION", "us-east-1")
    base_url = GENESYS_API_BASE.format(region=region)
    endpoint = f"{base_url}/api/v2/ai/assistant/tools"

    # Retrieve raw access token from SDK client for explicit HTTP request
    token = api_client._client_config._token_manager._access_token
    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json",
        "Accept": "application/json"
    }

    logger.info("HTTP POST %s", endpoint)
    logger.debug("Request Body: %s", json.dumps(tool_payload, indent=2))

    with httpx.Client(timeout=30.0) as client:
        response = client.post(endpoint, headers=headers, json=tool_payload)

        # Log full HTTP cycle
        logger.info("HTTP %s %s -> Status: %s", response.request.method, response.request.url, response.status_code)
        logger.debug("Response Body: %s", response.text)

        if response.status_code == 201:
            return response.json()
        elif response.status_code == 401:
            raise PermissionError("OAuth token expired or invalid. Refresh required.")
        elif response.status_code == 403:
            raise PermissionError("Client lacks ai:tool:write scope or tenant restriction applies.")
        elif response.status_code == 422:
            raise ValueError(f"Schema validation failed: {response.json().get('errors', 'Unknown validation error')}")
        elif response.status_code == 429:
            raise httpx.HTTPStatusError("Rate limit exceeded", request=response.request, response=response)
        else:
            response.raise_for_status()

def list_registered_tools(api_client: AiToolsApi, page_size: int = 25) -> List[Dict[str, Any]]:
    """
    Fetches registered tools with pagination support.
    """
    region = os.getenv("GENESYS_CLOUD_REGION", "us-east-1")
    base_url = GENESYS_API_BASE.format(region=region)
    endpoint = f"{base_url}/api/v2/ai/assistant/tools"
    token = api_client._client_config._token_manager._access_token
    headers = {"Authorization": f"Bearer {token}", "Accept": "application/json"}

    all_tools = []
    next_page_token: Optional[str] = None

    with httpx.Client(timeout=30.0) as client:
        while True:
            params = {"page_size": page_size}
            if next_page_token:
                params["next_page_token"] = next_page_token

            response = client.get(endpoint, headers=headers, params=params)
            response.raise_for_status()
            data = response.json()

            all_tools.extend(data.get("entities", []))
            next_page_token = data.get("next_page_token")
            
            if not next_page_token:
                break

    return all_tools

Step 3: Async Callback Execution with Circuit Breaker

The Genesys Cloud LLM gateway invokes your executionEndpoint asynchronously. Your endpoint must parse the incoming payload, execute the business logic, apply timeout management, and implement a circuit breaker to prevent cascading failures when external services degrade.

import asyncio
import json
from fastapi import FastAPI, Request
from pydantic import BaseModel, Field
from typing import Any, Dict
from tenacity import retry, stop_after_attempt, wait_fixed, retry_if_exception_type, circuit_breaker
from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry.exporter.prometheus import PrometheusMetricReader

# Initialize OpenTelemetry metrics
meter = metrics.get_meter("genesys_llm_tools")
invocation_latency = meter.create_histogram(
    name="genesys.tool.invocation.latency",
    unit="ms",
    description="Latency of tool invocations"
)
success_counter = meter.create_counter(
    name="genesys.tool.invocation.success",
    description="Successful tool invocations"
)
error_counter = meter.create_counter(
    name="genesys.tool.invocation.error",
    description="Failed tool invocations"
)

app = FastAPI()

class ToolInvocationPayload(BaseModel):
    tool_name: str
    parameters: Dict[str, Any]
    conversation_id: str = Field(..., description="Genesys Cloud conversation identifier")
    user_id: str = Field(..., description="Genesys Cloud user identifier")

class ToolResponse(BaseModel):
    output: Any
    status: str = "success"
    metadata: Dict[str, Any] = Field(default_factory=dict)

# Simulated external service call with circuit breaker protection
@retry(
    stop=stop_after_attempt(2),
    wait=wait_fixed(0.5),
    retry=retry_if_exception_type(asyncio.TimeoutError),
    reraise=True
)
@circuit_breaker(
    fail_max=5,
    exp_backoff=True,
    exp_backoff_max=60,
    exp_backoff_factor=2
)
async def execute_external_service(params: Dict[str, Any]) -> Dict[str, Any]:
    """
    Executes the actual business logic. Replaced with actual HTTP call or DB query.
    """
    # Simulate network latency
    await asyncio.sleep(0.1)
    
    # Simulate external service degradation
    if params.get("simulate_failure", False):
        raise ConnectionError("External service unavailable")
        
    return {"result": f"Processed {params.get('sku', 'unknown')}", "inventory_level": 42}

@app.post("/api/v1/tools/check_inventory")
async def handle_tool_invocation(request: Request):
    """
    Handles asynchronous callback registration from Genesys Cloud LLM Gateway.
    Implements timeout management, parsing, error mapping, and telemetry.
    """
    start_time = asyncio.get_event_loop().time()
    
    try:
        body = await request.json()
        invocation = ToolInvocationPayload(**body)
        
        # Apply strict timeout management (5 seconds)
        try:
            result = await asyncio.wait_for(
                execute_external_service(invocation.parameters),
                timeout=5.0
            )
        except asyncio.TimeoutError:
            error_counter.add(1, {"error_type": "timeout", "tool_name": invocation.tool_name})
            return {
                "output": None,
                "status": "error",
                "error_code": "TOOL_EXECUTION_TIMEOUT",
                "message": "Tool execution exceeded 5 second limit"
            }

        # Record success metrics
        latency_ms = (asyncio.get_event_loop().time() - start_time) * 1000
        invocation_latency.record(latency_ms, {"tool_name": invocation.tool_name})
        success_counter.add(1, {"tool_name": invocation.tool_name})
        
        # Write audit log for security governance
        audit_log = {
            "event": "TOOL_INVOCATION_SUCCESS",
            "tool_name": invocation.tool_name,
            "conversation_id": invocation.conversation_id,
            "user_id": invocation.user_id,
            "timestamp": asyncio.get_event_loop().time(),
            "parameters_hash": hash(json.dumps(invocation.parameters, sort_keys=True))
        }
        logger.info("AUDIT_LOG: %s", json.dumps(audit_log))

        return ToolResponse(output=result, status="success").model_dump()

    except json.JSONDecodeError:
        error_counter.add(1, {"error_type": "invalid_json", "tool_name": invocation.tool_name if "invocation" in locals() else "unknown"})
        return {"status": "error", "error_code": "INVALID_PAYLOAD", "message": "Malformed JSON in request body"}
    except Exception as e:
        latency_ms = (asyncio.get_event_loop().time() - start_time) * 1000
        invocation_latency.record(latency_ms, {"tool_name": invocation.tool_name if "invocation" in locals() else "unknown"})
        error_counter.add(1, {"error_type": type(e).__name__, "tool_name": invocation.tool_name if "invocation" in locals() else "unknown"})
        
        audit_log = {
            "event": "TOOL_INVOCATION_FAILURE",
            "tool_name": invocation.tool_name if "invocation" in locals() else "unknown",
            "error": str(e),
            "timestamp": asyncio.get_event_loop().time()
        }
        logger.error("AUDIT_LOG: %s", json.dumps(audit_log))
        
        return {
            "status": "error",
            "error_code": "EXECUTION_FAILURE",
            "message": "Internal service error during tool execution"
        }

Step 4: Telemetry, Audit Logging, and Response Parsing

The response parsing logic maps Genesys Cloud error codes to actionable data structures. The OpenTelemetry metrics export directly to Prometheus or Jaeger endpoints. Audit logs are written to structured JSON format for SIEM ingestion. The following configuration binds the metrics reader to your observability stack.

def configure_observability(prometheus_port: int = 9090) -> None:
    """
    Configures OpenTelemetry metrics export for AI observability platforms.
    """
    from opentelemetry.sdk.metrics import MeterProvider
    from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
    from opentelemetry.exporter.prometheus import PrometheusMetricReader
    import opentelemetry.metrics as metrics
    
    prom_reader = PrometheusMetricReader(port=prometheus_port)
    provider = MeterProvider(metric_readers=[prom_reader])
    metrics.set_meter_provider(provider)
    logger.info("OpenTelemetry metrics configured on port %s", prometheus_port)

The tool registry is exposed via a simple endpoint that returns the current state of registered tools. This enables extensible AI function orchestration by allowing external orchestrators to query available capabilities.

@app.get("/api/v1/tools/registry")
async def get_tool_registry():
    """
    Exposes the tool registry for extensible AI function orchestration.
    """
    api_client = initialize_genesys_client()
    tools = list_registered_tools(api_client, page_size=50)
    
    registry = {
        "total_tools": len(tools),
        "tools": [
            {
                "name": t.get("name"),
                "version": t.get("version"),
                "status": t.get("status"),
                "capabilities": list(t.get("schema", {}).get("properties", {}).keys())
            }
            for t in tools
        ]
    }
    return registry

Complete Working Example

The following script combines authentication, schema validation, registration, and the execution service into a single runnable module. Replace the environment variables with your tenant credentials before execution.

import os
import uvicorn
from main import initialize_genesys_client, build_tool_schema, register_tool, app, configure_observability

if __name__ == "__main__":
    # Configure observability stack
    configure_observability(prometheus_port=9090)

    # Initialize Genesys Cloud client
    ai_client = initialize_genesys_client()

    # Define tool parameters
    inventory_params = {
        "sku": {"type": "string", "description": "Product stock keeping unit", "required": True},
        "warehouse_id": {"type": "string", "description": "Target warehouse identifier", "required": False}
    }

    # Construct and validate schema
    tool_def = build_tool_schema(
        tool_name="check_inventory",
        description="Checks real-time inventory levels for a specific product SKU",
        parameters=inventory_params,
        execution_url="https://your-domain.com/api/v1/tools/check_inventory"
    )

    # Register with Genesys Cloud
    try:
        registration_result = register_tool(ai_client, tool_def)
        print(f"Tool registered successfully: {registration_result}")
    except Exception as e:
        print(f"Registration failed: {e}")

    # Start async callback server
    print("Starting FastAPI execution server on port 8000...")
    uvicorn.run(app, host="0.0.0.0", port=8000)

Common Errors & Debugging

Error: HTTP 401 Unauthorized

• What causes it: The OAuth token has expired, the client credentials are incorrect, or the token was revoked in the Genesys Cloud admin console.
• How to fix it: Verify GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET match the application configuration. The SDK automatically refreshes tokens, but manual restarts may require re-authentication.
• Code showing the fix: The initialize_genesys_client function validates scope presence and raises a clear exception if the token lacks ai:tool:write.

Error: HTTP 403 Forbidden

• What causes it: The OAuth application lacks the ai:tool:write scope, or the tenant restricts AI tool creation to specific user groups.
• How to fix it: Navigate to the Genesys Cloud admin console, open the OAuth application settings, and assign the ai:tool:write and ai:tool:read scopes. Verify the calling user belongs to an authorized role.
• Code showing the fix: The registration function explicitly checks for 403 status and raises a PermissionError with actionable guidance.

Error: HTTP 422 Unprocessable Entity

• What causes it: The JSON Schema violates draft 2020-12 rules, exceeds the 10-parameter limit, or contains unsupported data types.
• How to fix it: Review the build_tool_schema validation logic. Ensure all parameter types belong to ["string", "number", "integer", "boolean", "array", "object"]. Remove deprecated schema keywords like additionalItems or dependencies.
• Code showing the fix: The jsonschema.Draft202012Validator.check_schema call catches structural violations before the API request is sent.

Error: HTTP 429 Too Many Requests

• What causes it: The tenant enforces rate limits on tool registration or listing endpoints.
• How to fix it: Implement exponential backoff. The tenacity decorator in register_tool automatically retries with increasing delays up to 10 seconds.
• Code showing the fix: The @retry configuration handles 429 responses transparently without blocking the main thread.

Error: Circuit Breaker Open

• What causes it: The external service invoked by the tool has degraded, causing repeated failures that trigger the circuit breaker threshold.
• How to fix it: Wait for the circuit breaker recovery window, verify external service health, and inspect the audit logs for root cause analysis.
• Code showing the fix: The @circuit_breaker decorator halts execution after 5 consecutive failures and enters an exponential backoff state until the service recovers.

Official References

• Genesys Cloud AI Assistant Tools API Documentation
• Genesys Cloud Python SDK Reference
• JSON Schema Draft 2020-12 Specification
• OpenTelemetry Python Metrics Documentation