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ServiceNow MCP Server

by javerthl

list_changesets

Retrieve and filter ServiceNow changesets by state, application, developer, or timeframe to monitor and manage system modifications.

Instructions

List changesets from ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
applicationNoFilter by application
developerNoFilter by developer
limitNoMaximum number of records to return
offsetNoOffset to start from
queryNoAdditional query string
stateNoFilter by state
timeframeNoFilter by timeframe (recent, last_week, last_month)

Implementation Reference

  • The core handler function that implements the logic for listing changesets. It validates input parameters, constructs the appropriate ServiceNow Table API query for the sys_update_set table, makes the HTTP GET request, and returns the results or error.
    def list_changesets( auth_manager: AuthManager, server_config: ServerConfig, params: Union[Dict[str, Any], ListChangesetsParams], ) -> Dict[str, Any]: """ List changesets from ServiceNow. Args: auth_manager: The authentication manager. server_config: The server configuration. params: The parameters for listing changesets. Can be a dictionary or a ListChangesetsParams object. Returns: A list of changesets. """ # Unwrap and validate parameters result = _unwrap_and_validate_params(params, ListChangesetsParams) if not result["success"]: return result validated_params = result["params"] # 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", } # Build query parameters query_params = { "sysparm_limit": validated_params.limit, "sysparm_offset": validated_params.offset, } # Build sysparm_query query_parts = [] if validated_params.state: query_parts.append(f"state={validated_params.state}") if validated_params.application: query_parts.append(f"application={validated_params.application}") if validated_params.developer: query_parts.append(f"developer={validated_params.developer}") if validated_params.timeframe: if validated_params.timeframe == "recent": query_parts.append("sys_created_onONLast 7 days@javascript:gs.beginningOfLast7Days()@javascript:gs.endOfToday()") elif validated_params.timeframe == "last_week": query_parts.append("sys_created_onONLast week@javascript:gs.beginningOfLastWeek()@javascript:gs.endOfLastWeek()") elif validated_params.timeframe == "last_month": query_parts.append("sys_created_onONLast month@javascript:gs.beginningOfLastMonth()@javascript:gs.endOfLastMonth()") if validated_params.query: query_parts.append(validated_params.query) if query_parts: query_params["sysparm_query"] = "^".join(query_parts) # Make the API request url = f"{instance_url}/api/now/table/sys_update_set" try: response = requests.get(url, params=query_params, headers=headers) response.raise_for_status() result = response.json() return { "success": True, "changesets": result.get("result", []), "count": len(result.get("result", [])), } except requests.exceptions.RequestException as e: logger.error(f"Error listing changesets: {e}") return { "success": False, "message": f"Error listing changesets: {str(e)}", }
  • Pydantic BaseModel defining the input schema for the list_changesets tool, including optional filters like limit, offset, state, application, developer, timeframe, and custom query.
    class ListChangesetsParams(BaseModel): """Parameters for listing changesets.""" 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") application: Optional[str] = Field(None, description="Filter by application") developer: Optional[str] = Field(None, description="Filter by developer") timeframe: Optional[str] = Field(None, description="Filter by timeframe (recent, last_week, last_month)") query: Optional[str] = Field(None, description="Additional query string")
  • Tool registration entry in get_tool_definitions() function, mapping the tool name to its handler (list_changesets_tool), input schema model (ListChangesetsParams), return type hint, description, and serialization instruction. This dictionary is used by the MCP server to expose the tool and handle calls.
    "list_changesets": ( list_changesets_tool, ListChangesetsParams, str, # Expects JSON string "List changesets from ServiceNow", "json", # Tool returns list/dict ),
  • Helper function used by the handler to unwrap dictionary parameters into a Pydantic model instance, validate them, and check for required fields. Called at the start of list_changesets.
    def _unwrap_and_validate_params( params: Union[Dict[str, Any], BaseModel], model_class: Type[T], required_fields: Optional[List[str]] = None ) -> Dict[str, Any]: """ Unwrap and validate parameters. Args: params: The parameters to unwrap and validate. Can be a dictionary or a Pydantic model. model_class: The Pydantic model class to validate against. required_fields: List of fields that must be present. Returns: A dictionary with success status and validated parameters or error message. """ try: # Handle case where params is already a Pydantic model if isinstance(params, BaseModel): # If it's already the correct model class, use it directly if isinstance(params, model_class): model_instance = params # Otherwise, convert to dict and create new instance else: model_instance = model_class(**params.dict()) # Handle dictionary case else: # Create model instance model_instance = model_class(**params) # Check required fields if required_fields: missing_fields = [] for field in required_fields: if getattr(model_instance, field, None) is None: missing_fields.append(field) if missing_fields: return { "success": False, "message": f"Missing required fields: {', '.join(missing_fields)}", } return { "success": True, "params": model_instance, } except Exception as e: return { "success": False, "message": f"Invalid parameters: {str(e)}", }

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