list_story_dependencies
Retrieve story dependencies in ServiceNow to identify relationships between agile stories and manage project workflows effectively.
Instructions
List story dependencies from ServiceNow
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of records to return | |
| offset | No | Offset to start from | |
| query | No | Additional query string | |
| dependent_story | No | Sys_id of the dependent story is required | |
| prerequisite_story | No | Sys_id that this story depends on is required |
Implementation Reference
- The handler function that executes the tool: validates parameters, builds ServiceNow query for story dependencies table, makes GET request to /api/now/table/m2m_story_dependencies, and returns the list of dependencies.
def list_story_dependencies( auth_manager: AuthManager, server_config: ServerConfig, params: Dict[str, Any], ) -> Dict[str, Any]: """ List story dependencies from ServiceNow. Args: auth_manager: The authentication manager. server_config: The server configuration. params: The parameters for listing story dependencies. Returns: A list of story dependencies. """ # Unwrap and validate parameters result = _unwrap_and_validate_params( params, ListStoryDependenciesParams ) if not result["success"]: return result validated_params = result["params"] # Build the query query_parts = [] if validated_params.dependent_story: query_parts.append(f"dependent_story={validated_params.dependent_story}") if validated_params.prerequisite_story: query_parts.append(f"prerequisite_story={validated_params.prerequisite_story}") # 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/m2m_story_dependencies" 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 story_dependencies = result.get("result", []) count = len(story_dependencies) return { "success": True, "story_dependencies": story_dependencies, "count": count, "total": count, # Use count as total if total is not provided } except requests.exceptions.RequestException as e: logger.error(f"Error listing story dependencies: {e}") return { "success": False, "message": f"Error listing story dependencies: {str(e)}", } - Pydantic model defining the input parameters for the list_story_dependencies tool.
class ListStoryDependenciesParams(BaseModel): """Parameters for listing story dependencies.""" limit: Optional[int] = Field(10, description="Maximum number of records to return") offset: Optional[int] = Field(0, description="Offset to start from") query: Optional[str] = Field(None, description="Additional query string") dependent_story: Optional[str] = Field(None, description="Sys_id of the dependent story is required") prerequisite_story: Optional[str] = Field(None, description="Sys_id that this story depends on is required") - src/servicenow_mcp/utils/tool_utils.py:858-864 (registration)Registers the tool in the central tool_definitions dictionary used by the MCP server, mapping name to (handler, schema, return_type, description, serialization). Note: list_story_dependencies_tool is alias imported from story_tools.py
"list_story_dependencies": ( list_story_dependencies_tool, ListStoryDependenciesParams, str, # Expects JSON string "List story dependencies from ServiceNow", "json", # Tool returns list/dict ), - src/servicenow_mcp/tools/__init__.py:96-205 (registration)Imports and exposes the list_story_dependencies function in the tools package __init__ for easy access.
list_story_dependencies, create_story_dependency, delete_story_dependency, ) from servicenow_mcp.tools.epic_tools import ( create_epic, update_epic, list_epics, ) from servicenow_mcp.tools.scrum_task_tools import ( create_scrum_task, update_scrum_task, list_scrum_tasks, ) from servicenow_mcp.tools.project_tools import ( create_project, update_project, list_projects, ) # from servicenow_mcp.tools.problem_tools import create_problem, update_problem # from servicenow_mcp.tools.request_tools import create_request, update_request __all__ = [ # Incident tools "create_incident", "update_incident", "add_comment", "resolve_incident", "list_incidents", # Catalog tools "list_catalog_items", "get_catalog_item", "list_catalog_categories", "create_catalog_category", "update_catalog_category", "move_catalog_items", "get_optimization_recommendations", "update_catalog_item", "create_catalog_item_variable", "list_catalog_item_variables", "update_catalog_item_variable", # Change management tools "create_change_request", "update_change_request", "list_change_requests", "get_change_request_details", "add_change_task", "submit_change_for_approval", "approve_change", "reject_change", # Workflow management tools "list_workflows", "get_workflow_details", "list_workflow_versions", "get_workflow_activities", "create_workflow", "update_workflow", "activate_workflow", "deactivate_workflow", "add_workflow_activity", "update_workflow_activity", "delete_workflow_activity", "reorder_workflow_activities", # Changeset tools "list_changesets", "get_changeset_details", "create_changeset", "update_changeset", "commit_changeset", "publish_changeset", "add_file_to_changeset", # Script Include tools "list_script_includes", "get_script_include", "create_script_include", "update_script_include", "delete_script_include", # Knowledge Base tools "create_knowledge_base", "list_knowledge_bases", "create_category", "list_categories", "create_article", "update_article", "publish_article", "list_articles", "get_article", # User management tools "create_user", "update_user", "get_user", "list_users", "create_group", "update_group", "add_group_members", "remove_group_members", "list_groups", # Story tools "create_story", "update_story", "list_stories", "list_story_dependencies", - Helper function used by the handler (and other tools) to unwrap, validate parameters against Pydantic schema, and handle common input formats.
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)}", }