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create_workflow

Automate workflow creation in ServiceNow by defining active status, name, description, table, and additional attributes for streamlined process management.

Instructions

Create a new workflow in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Implementation Reference

  • The handler function that executes the create_workflow tool logic, unwrapping params, validating, preparing data, and posting to ServiceNow wf_workflow endpoint.
    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")
  • Registers the 'create_workflow' tool in get_tool_definitions, mapping name to handler function, params schema, 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 ),
  • Imports create_workflow (line 82) from workflow_tools.py into the tools package namespace for exposure.
    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 in create_workflow to unwrap Pydantic model params into dict.
    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

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