list_change_requests
Retrieve and filter change requests from ServiceNow to monitor IT infrastructure modifications, track progress, and manage approvals.
Instructions
List change requests from ServiceNow
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of records to return | |
| offset | No | Offset to start from | |
| state | No | Filter by state | |
| type | No | Filter by type (normal, standard, emergency) | |
| category | No | Filter by category | |
| assignment_group | No | Filter by assignment group | |
| timeframe | No | Filter by timeframe (upcoming, in-progress, completed) | |
| query | No | Additional query string |
Implementation Reference
- Main handler function that executes the list_change_requests tool: validates input params using ListChangeRequestsParams, constructs ServiceNow Table API query based on filters, fetches change requests via GET request, and returns formatted result.
def list_change_requests( auth_manager: AuthManager, server_config: ServerConfig, params: Dict[str, Any], ) -> Dict[str, Any]: """ List change requests from ServiceNow. Args: auth_manager: The authentication manager. server_config: The server configuration. params: The parameters for listing change requests. Returns: A list of change requests. """ # Unwrap and validate parameters result = _unwrap_and_validate_params( params, ListChangeRequestsParams ) if not result["success"]: return result validated_params = result["params"] # Build the query query_parts = [] if validated_params.state: query_parts.append(f"state={validated_params.state}") if validated_params.type: query_parts.append(f"type={validated_params.type}") if validated_params.category: query_parts.append(f"category={validated_params.category}") if validated_params.assignment_group: query_parts.append(f"assignment_group={validated_params.assignment_group}") # Handle timeframe filtering if validated_params.timeframe: now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") if validated_params.timeframe == "upcoming": query_parts.append(f"start_date>{now}") elif validated_params.timeframe == "in-progress": query_parts.append(f"start_date<{now}^end_date>{now}") elif validated_params.timeframe == "completed": query_parts.append(f"end_date<{now}") # Add any additional query string if validated_params.query: query_parts.append(validated_params.query) # Combine query parts query = "^".join(query_parts) if query_parts else "" # Get the instance URL instance_url = _get_instance_url(auth_manager, server_config) if not instance_url: return { "success": False, "message": "Cannot find instance_url in either server_config or auth_manager", } # Get the headers headers = _get_headers(auth_manager, server_config) if not headers: return { "success": False, "message": "Cannot find get_headers method in either auth_manager or server_config", } # Make the API request url = f"{instance_url}/api/now/table/change_request" params = { "sysparm_limit": validated_params.limit, "sysparm_offset": validated_params.offset, "sysparm_query": query, "sysparm_display_value": "true", } try: response = requests.get(url, headers=headers, params=params) response.raise_for_status() result = response.json() # Handle the case where result["result"] is a list change_requests = result.get("result", []) count = len(change_requests) return { "success": True, "change_requests": change_requests, "count": count, "total": count, # Use count as total if total is not provided } except requests.exceptions.RequestException as e: logger.error(f"Error listing change requests: {e}") return { "success": False, "message": f"Error listing change requests: {str(e)}", } - Pydantic BaseModel defining the input schema/parameters for the list_change_requests tool, including pagination (limit/offset), filters (state, type, etc.), and custom query.
class ListChangeRequestsParams(BaseModel): """Parameters for listing change requests.""" limit: Optional[int] = Field(10, description="Maximum number of records to return") offset: Optional[int] = Field(0, description="Offset to start from") state: Optional[str] = Field(None, description="Filter by state") type: Optional[str] = Field(None, description="Filter by type (normal, standard, emergency)") category: Optional[str] = Field(None, description="Filter by category") assignment_group: Optional[str] = Field(None, description="Filter by assignment group") timeframe: Optional[str] = Field(None, description="Filter by timeframe (upcoming, in-progress, completed)") query: Optional[str] = Field(None, description="Additional query string") - src/servicenow_mcp/utils/tool_utils.py:493-499 (registration)Tool registration/definition in get_tool_definitions() dict: maps 'list_change_requests' to its handler (aliased), schema (ListChangeRequestsParams), description, and serialization handling for MCP server.
"list_change_requests": ( list_change_requests_tool, ListChangeRequestsParams, str, # Expects JSON string "List change requests from ServiceNow", "json", # Tool returns list/dict ), - src/servicenow_mcp/tools/__init__.py:23-32 (registration)Import and export of list_change_requests function from change_tools.py into tools package __init__, making it available for registration.
from servicenow_mcp.tools.change_tools import ( add_change_task, approve_change, create_change_request, get_change_request_details, list_change_requests, reject_change, submit_change_for_approval, update_change_request, ) - Shared helper function used by list_change_requests (and other tools) to unwrap, validate input params against Pydantic model, and handle common formatting issues.
def _unwrap_and_validate_params(params: Any, model_class: Type[T], required_fields: List[str] = None) -> Dict[str, Any]: """ Helper function to unwrap and validate parameters. Args: params: The parameters to unwrap and validate. model_class: The Pydantic model class to validate against. required_fields: List of required field names. Returns: A tuple of (success, result) where result is either the validated parameters or an error message. """ # Handle case where params might be wrapped in another dictionary if isinstance(params, dict) and len(params) == 1 and "params" in params and isinstance(params["params"], dict): logger.warning("Detected params wrapped in a 'params' key. Unwrapping...") params = params["params"] # Handle case where params might be a Pydantic model object if not isinstance(params, dict): try: # Try to convert to dict if it's a Pydantic model logger.warning("Params is not a dictionary. Attempting to convert...") params = params.dict() if hasattr(params, "dict") else dict(params) except Exception as e: logger.error(f"Failed to convert params to dictionary: {e}") return { "success": False, "message": f"Invalid parameters format. Expected a dictionary, got {type(params).__name__}", } # Validate required parameters are present if required_fields: for field in required_fields: if field not in params: return { "success": False, "message": f"Missing required parameter '{field}'", } try: # Validate parameters against the model validated_params = model_class(**params) return { "success": True, "params": validated_params, } except Exception as e: logger.error(f"Error validating parameters: {e}") return { "success": False, "message": f"Error validating parameters: {str(e)}", }