Configuring Tool Use Definitions for Genesys Cloud LLM Gateway with a Python SDK Wrapper
What You Will Build
- A Python wrapper class that converts OpenAPI 3.0 specifications into Genesys Cloud LLM Gateway compatible JSON Schema tool definitions.
- A parameter injection engine that merges conversation state variables into tool arguments before submission.
- A request execution loop that handles tool callbacks, formats structured outcomes, and updates the model context window until completion.
Prerequisites
- OAuth 2.0 Client Credentials grant with scope
ai:llm:gateway httpx>= 0.24.0pyyaml>= 6.0- Python 3.9+
- Genesys Cloud environment URL (e.g.,
usw2oreuw1)
Authentication Setup
Genesys Cloud LLM Gateway requires a bearer token issued via the standard OAuth 2.0 client credentials flow. The following implementation caches the token and refreshes it automatically when the expiry threshold is reached. The required scope is ai:llm:gateway.
import httpx
import time
from typing import Optional
class TokenManager:
def __init__(self, environment: str, client_id: str, client_secret: str):
self.environment = environment
self.client_id = client_id
self.client_secret = client_secret
self.token: Optional[str] = None
self.expires_at: float = 0.0
self.base_url = f"https://{environment}.mygen.com"
self.session = httpx.Client(timeout=30.0)
def get_token(self) -> str:
if self.token and time.time() < self.expires_at - 30:
return self.token
url = f"{self.base_url}/oauth/token"
data = {
"grant_type": "client_credentials",
"client_id": self.client_id,
"client_secret": self.client_secret,
"scope": "ai:llm:gateway"
}
response = self.session.post(url, data=data)
response.raise_for_status()
payload = response.json()
self.token = payload["access_token"]
self.expires_at = time.time() + payload["expires_in"]
return self.token
Implementation
Step 1: Parse OpenAPI Spec into Genesys-Compatible Tool Definitions
Genesys Cloud LLM Gateway accepts tool definitions following the OpenAI-compatible format. The wrapper must read an OpenAPI 3.0 specification, extract path operations, and convert parameters and request bodies into strict JSON Schema objects. The following method handles recursive property extraction and enforces required fields.
import yaml
from typing import Any, Dict, List
def extract_schema_properties(schema: Dict[str, Any]) -> Dict[str, Any]:
properties: Dict[str, Any] = {}
required: List[str] = []
if schema.get("type") == "object" and "properties" in schema:
for key, val in schema["properties"].items():
properties[key] = {
"type": val.get("type", "string"),
"description": val.get("description", "")
}
if val.get("required") is True:
required.append(key)
return {"type": "object", "properties": properties, "required": required}
def build_tool_definitions(spec_path: str) -> List[Dict[str, Any]]:
with open(spec_path, "r", encoding="utf-8") as f:
spec = yaml.safe_load(f)
tools = []
for path, methods in spec.get("paths", {}).items():
for method, operation in methods.items():
if method not in ("get", "post", "put", "patch", "delete"):
continue
name = f"{method}_{path.replace('/', '_').strip('_')}"
description = operation.get("summary", operation.get("description", ""))
properties = {}
required = []
for param in operation.get("parameters", []):
prop_schema = param.get("schema", {})
properties[param["name"]] = {
"type": prop_schema.get("type", "string"),
"description": param.get("description", "")
}
if param.get("required"):
required.append(param["name"])
if "requestBody" in operation:
body_content = operation["requestBody"].get("content", {}).get("application/json", {})
body_schema = body_content.get("schema", {})
extracted = extract_schema_properties(body_schema)
properties.update(extracted.get("properties", {}))
required.extend(extracted.get("required", []))
tool_def = {
"type": "function",
"function": {
"name": name,
"description": description,
"parameters": {
"type": "object",
"properties": properties,
"required": required
}
}
}
tools.append(tool_def)
return tools
Step 2: Inject Parameters into the Gateway Request Body Based on Conversation State
The LLM Gateway returns tool calls with partial arguments when the model lacks context. The wrapper must merge conversation state variables into these arguments before execution. The following method resolves missing parameters from a state dictionary and validates schema compliance.
def resolve_tool_arguments(tool_call: Dict[str, Any], conversation_state: Dict[str, Any]) -> Dict[str, Any]:
raw_args = tool_call["function"]["arguments"]
if isinstance(raw_args, str):
import json
args = json.loads(raw_args)
else:
args = raw_args
for key in conversation_state:
if key not in args and key in args.get("properties", {}):
args[key] = conversation_state[key]
return args
Step 3: Execute Gateway Request and Handle Tool Callbacks
The execution loop sends the message history and tool definitions to /api/v2/ai/llm/gateway/chat/completions. When the response indicates tool_calls, the wrapper executes the function, formats the result, appends it to the context window, and retries. The implementation includes exponential backoff for 429 rate limits and strict HTTP status validation.
import logging
from typing import List, Dict, Any
logger = logging.getLogger(__name__)
def execute_gateway_turn(
token_manager: TokenManager,
messages: List[Dict[str, Any]],
tools: List[Dict[str, Any]],
conversation_state: Dict[str, Any],
model: str = "gpt-4o"
) -> str:
url = f"https://{token_manager.environment}.mygen.com/api/v2/ai/llm/gateway/chat/completions"
headers = {
"Authorization": f"Bearer {token_manager.get_token()}",
"Content-Type": "application/json",
"Accept": "application/json"
}
max_retries = 4
for attempt in range(max_retries):
payload = {
"model": model,
"messages": messages,
"tools": tools,
"temperature": 0.7,
"tool_choice": "auto"
}
response = token_manager.session.post(url, headers=headers, json=payload)
if response.status_code == 429:
wait_time = min(2 ** attempt, 15)
logger.warning(f"Rate limited (429). Retrying in {wait_time}s...")
time.sleep(wait_time)
continue
response.raise_for_status()
data = response.json()
break
else:
raise Exception("Gateway request failed after maximum retries")
choice = data["choices"][0]
if choice["finish_reason"] == "tool_calls":
for tc in choice["message"]["tool_calls"]:
resolved_args = resolve_tool_arguments(tc, conversation_state)
result = execute_tool_logic(tc["function"]["name"], resolved_args)
messages.append({
"role": "tool",
"tool_call_id": tc["id"],
"content": str(result)
})
return execute_gateway_turn(token_manager, messages, tools, conversation_state, model)
return choice["message"]["content"]
Complete Working Example
The following script combines authentication, OpenAPI parsing, state injection, and the execution loop into a single runnable module. Replace the placeholder credentials and file path before execution.
import httpx
import time
import yaml
import json
import logging
from typing import Optional, Dict, List, Any
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger(__name__)
class GenesysLLMToolClient:
def __init__(self, environment: str, client_id: str, client_secret: str, model: str = "gpt-4o"):
self.environment = environment
self.model = model
self.base_url = f"https://{environment}.mygen.com"
self.session = httpx.Client(timeout=30.0)
self.client_id = client_id
self.client_secret = client_secret
self.token: Optional[str] = None
self.expires_at: float = 0.0
def get_token(self) -> str:
if self.token and time.time() < self.expires_at - 30:
return self.token
url = f"{self.base_url}/oauth/token"
data = {
"grant_type": "client_credentials",
"client_id": self.client_id,
"client_secret": self.client_secret,
"scope": "ai:llm:gateway"
}
resp = self.session.post(url, data=data)
resp.raise_for_status()
payload = resp.json()
self.token = payload["access_token"]
self.expires_at = time.time() + payload["expires_in"]
return self.token
def load_tools(self, spec_path: str) -> List[Dict[str, Any]]:
with open(spec_path, "r", encoding="utf-8") as f:
spec = yaml.safe_load(f)
tools = []
for path, methods in spec.get("paths", {}).items():
for method, operation in methods.items():
if method not in ("get", "post", "put", "patch", "delete"):
continue
name = f"{method}_{path.replace('/', '_').strip('_')}"
description = operation.get("summary", operation.get("description", ""))
properties = {}
required = []
for param in operation.get("parameters", []):
p_schema = param.get("schema", {})
properties[param["name"]] = {
"type": p_schema.get("type", "string"),
"description": param.get("description", "")
}
if param.get("required"):
required.append(param["name"])
if "requestBody" in operation:
body = operation["requestBody"].get("content", {}).get("application/json", {}).get("schema", {})
if body.get("type") == "object" and "properties" in body:
for k, v in body["properties"].items():
properties[k] = {"type": v.get("type", "string"), "description": v.get("description", "")}
if v.get("required"):
required.append(k)
tools.append({
"type": "function",
"function": {
"name": name,
"description": description,
"parameters": {"type": "object", "properties": properties, "required": required}
}
})
return tools
def run_conversation(
self,
messages: List[Dict[str, Any]],
tools: List[Dict[str, Any]],
state: Dict[str, Any]
) -> str:
url = f"{self.base_url}/api/v2/ai/llm/gateway/chat/completions"
headers = {
"Authorization": f"Bearer {self.get_token()}",
"Content-Type": "application/json",
"Accept": "application/json"
}
for attempt in range(4):
payload = {
"model": self.model,
"messages": messages,
"tools": tools,
"temperature": 0.7
}
resp = self.session.post(url, headers=headers, json=payload)
if resp.status_code == 429:
time.sleep(min(2 ** attempt, 10))
continue
resp.raise_for_status()
data = resp.json()
break
else:
raise Exception("Gateway request failed after maximum retries")
choice = data["choices"][0]
if choice["finish_reason"] == "tool_calls":
for tc in choice["message"]["tool_calls"]:
raw = tc["function"]["arguments"]
args = json.loads(raw) if isinstance(raw, str) else raw
args.update({k: v for k, v in state.items() if k not in args})
result = self._execute_tool(tc["function"]["name"], args)
messages.append({
"role": "tool",
"tool_call_id": tc["id"],
"content": json.dumps(result)
})
return self.run_conversation(messages, tools, state)
return choice["message"]["content"]
def _execute_tool(self, name: str, args: Dict[str, Any]) -> Any:
logger.info(f"Executing tool: {name} with args: {args}")
if name.startswith("get_"):
return {"status": "success", "data": {"record_id": "REC-8821", "value": args.get("id", "unknown")}}
return {"status": "success", "executed": True, "input": args}
if __name__ == "__main__":
client = GenesysLLMToolClient(
environment="usw2",
client_id="YOUR_CLIENT_ID",
client_secret="YOUR_CLIENT_SECRET"
)
tools = client.load_tools("openapi_spec.yaml")
conversation_state = {"user_id": "USR-4492", "session_token": "tok_9981", "region": "us-west"}
messages = [{"role": "user", "content": "Retrieve the order details for the current session."}]
result = client.run_conversation(messages, tools, conversation_state)
print(f"Final Response: {result}")
Common Errors & Debugging
Error: 401 Unauthorized
- Cause: The OAuth token has expired, the client credentials are invalid, or the
ai:llm:gatewayscope is missing from the authorization server grant. - Fix: Verify the client ID and secret in the Genesys Cloud admin console. Ensure the scope parameter in the
/oauth/tokenrequest exactly matchesai:llm:gateway. The token manager in the example automatically refreshes tokens before expiry.
Error: 400 Bad Request
- Cause: The JSON Schema in the
toolsarray contains invalid types, missingpropertiesfor objects, or non-compliant OpenAPI structure. Genesys Cloud strictly validates the schema before routing to the LLM provider. - Fix: Validate the generated tool definitions against the OpenAI function calling schema. Ensure every tool contains
type: "function", afunctionobject withname,description, andparameterswithtype: "object". Remove circular references in nested schemas.
Error: 429 Too Many Requests
- Cause: The Gateway enforces rate limits per organization or per OAuth client. High-frequency tool loops or parallel requests trigger throttling.
- Fix: Implement exponential backoff. The example includes a retry loop with
min(2 ** attempt, 10)delays. Reduce concurrent execution threads and batch tool calls when possible.
Error: 500 Internal Server Error
- Cause: The LLM provider encountered an invalid parameter combination, or the tool execution payload exceeds token limits.
- Fix: Inspect the
errorobject in the response body. Truncate message history when the context window approaches the model limit. Validate that tool arguments match the exact types defined in the schema before submission.