create_workflow
Design and implement new workflows in ServiceNow with structured parameters, including name, description, table association, and active status, to streamline business processes.
Instructions
Create a new workflow in ServiceNow
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| active | No | Whether the workflow is active | |
| attributes | No | Additional attributes for the workflow | |
| description | No | Description of the workflow | |
| name | Yes | Name of the workflow | |
| table | No | Table the workflow applies to |
Implementation Reference
- The main handler function that implements the create_workflow tool. It unwraps parameters, validates input, prepares the payload, and makes a POST request to the ServiceNow wf_workflow table API to create the workflow.def create_workflow( auth_manager: AuthManager, server_config: ServerConfig, params: Dict[str, Any], ) -> Dict[str, Any]: """ Create a new workflow in ServiceNow. Args: auth_manager: Authentication manager server_config: Server configuration params: Parameters for creating a workflow Returns: Dict[str, Any]: Created workflow details """ # Unwrap parameters if needed params = _unwrap_params(params, CreateWorkflowParams) # Get the correct auth_manager and server_config try: auth_manager, server_config = _get_auth_and_config(auth_manager, server_config) except ValueError as e: logger.error(f"Error getting auth and config: {e}") return {"error": str(e)} # Validate required parameters if not params.get("name"): return {"error": "Workflow name is required"} # Prepare data for the API request data = { "name": params["name"], } if params.get("description"): data["description"] = params["description"] if params.get("table"): data["table"] = params["table"] if params.get("active") is not None: data["active"] = str(params["active"]).lower() if params.get("attributes"): # Add any additional attributes data.update(params["attributes"]) # Make the API request try: headers = auth_manager.get_headers() url = f"{server_config.instance_url}/api/now/table/wf_workflow" response = requests.post(url, headers=headers, json=data) response.raise_for_status() result = response.json() return { "workflow": result.get("result", {}), "message": "Workflow created successfully", } except requests.RequestException as e: logger.error(f"Error creating workflow: {e}") return {"error": str(e)} except Exception as e: logger.error(f"Unexpected error creating workflow: {e}") return {"error": str(e)}
- Pydantic BaseModel defining the input schema/parameters for the create_workflow tool.class CreateWorkflowParams(BaseModel): """Parameters for creating a new workflow.""" name: str = Field(..., description="Name of the workflow") description: Optional[str] = Field(None, description="Description of the workflow") table: Optional[str] = Field(None, description="Table the workflow applies to") active: Optional[bool] = Field(True, description="Whether the workflow is active") attributes: Optional[Dict[str, Any]] = Field(None, description="Additional attributes for the workflow")
- src/servicenow_mcp/utils/tool_utils.py:518-523 (registration)Registration of the create_workflow tool in the get_tool_definitions dictionary, specifying the handler alias, params model, return type hint, description, and serialization method."create_workflow": ( create_workflow_tool, CreateWorkflowParams, str, # Expects JSON string "Create a new workflow in ServiceNow", "json_dict", # Tool returns Pydantic model
- src/servicenow_mcp/tools/__init__.py:78-91 (registration)Import and re-export of workflow tools including create_workflow in the tools package __init__.from servicenow_mcp.tools.workflow_tools import ( activate_workflow, add_workflow_activity, create_workflow, deactivate_workflow, delete_workflow_activity, get_workflow_activities, get_workflow_details, list_workflow_versions, list_workflows, reorder_workflow_activities, update_workflow, update_workflow_activity, )
- Helper function used by create_workflow to unwrap and normalize input parameters using the CreateWorkflowParams model.def _unwrap_params(params: Any, param_class: Type[T]) -> Dict[str, Any]: """ Unwrap parameters if they're wrapped in a Pydantic model. This helps handle cases where the parameters are passed as a model instead of a dict. """ if isinstance(params, dict): return params if isinstance(params, param_class): return params.dict(exclude_none=True) return params