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JLKmach

ServiceNow MCP Server

by JLKmach

create_epic

Create new epics in ServiceNow to organize large work initiatives, track progress, and manage agile project components with priority, assignment, and detailed descriptions.

Instructions

Create a new epic in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
short_descriptionYesShort description of the epic
descriptionNoDetailed description of the epic
priorityNoPriority of epic (1 is Critical, 2 is High, 3 is Moderate, 4 is Low, 5 is Planning)
stateNoState of story (-6 is Draft,1 is Ready,2 is Work in progress, 3 is Complete, 4 is Cancelled)
assignment_groupNoGroup assigned to the epic
assigned_toNoUser assigned to the epic
work_notesNoWork notes to add to the epic. Used for adding notes and comments to an epic

Implementation Reference

  • The main handler function that implements the create_epic tool logic, including parameter validation, API request preparation, and ServiceNow REST API call to create an epic.
    def create_epic(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Create a new epic in ServiceNow.
    
        Args:
            auth_manager: The authentication manager.
            server_config: The server configuration.
            params: The parameters for creating the epic.
    
        Returns:
            The created epic.
        """
    
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            CreateEpicParams, 
            required_fields=["short_description"]
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # Prepare the request data
        data = {
            "short_description": validated_params.short_description,
        }
           
        # Add optional fields if provided
        if validated_params.description:
            data["description"] = validated_params.description
        if validated_params.priority:
            data["priority"] = validated_params.priority
        if validated_params.assignment_group:
            data["assignment_group"] = validated_params.assignment_group
        if validated_params.assigned_to:
            data["assigned_to"] = validated_params.assigned_to
        if validated_params.work_notes:
            data["work_notes"] = validated_params.work_notes
        
        # 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",
            }
        
        # Add Content-Type header
        headers["Content-Type"] = "application/json"
        
        # Make the API request
        url = f"{instance_url}/api/now/table/rm_epic"
        
        try:
            response = requests.post(url, json=data, headers=headers)
            response.raise_for_status()
            
            result = response.json()
            
            return {
                "success": True,
                "message": "Epic created successfully",
                "epic": result["result"],
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error creating epic: {e}")
            return {
                "success": False,
                "message": f"Error creating epic: {str(e)}",
            }
  • Pydantic BaseModel defining the input schema/parameters for the create_epic tool.
    class CreateEpicParams(BaseModel):
        """Parameters for creating an epic."""
    
        short_description: str = Field(..., description="Short description of the epic")
        description: Optional[str] = Field(None, description="Detailed description of the epic")
        priority: Optional[str] = Field(None, description="Priority of epic (1 is Critical, 2 is High, 3 is Moderate, 4 is Low, 5 is Planning)")
        state: Optional[str] = Field(None, description="State of story (-6 is Draft,1 is Ready,2 is Work in progress, 3 is Complete, 4 is Cancelled)")
        assignment_group: Optional[str] = Field(None, description="Group assigned to the epic")
        assigned_to: Optional[str] = Field(None, description="User assigned to the epic")
        work_notes: Optional[str] = Field(None, description="Work notes to add to the epic. Used for adding notes and comments to an epic")
        
    class UpdateEpicParams(BaseModel):
  • Registration of the create_epic tool in the central tool_definitions dictionary, mapping name to handler, schema, return type, description, and serialization method.
    "create_epic": (
        create_epic_tool,
        CreateEpicParams,
        str,
        "Create a new epic in ServiceNow",
        "str",
    ),
  • Import of the create_epic handler aliased as create_epic_tool for use in tool registration.
    from servicenow_mcp.tools.epic_tools import (
        create_epic as create_epic_tool,
        update_epic as update_epic_tool,
        list_epics as list_epics_tool,
    )
  • Helper function used by create_epic to unwrap, validate input parameters against CreateEpicParams schema.
    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)}",
            }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Create' implies a write/mutation operation, the description doesn't address permissions required, whether creation is reversible, what happens on success/failure, rate limits, or what the tool returns. For a mutation tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a tool with comprehensive schema documentation and gets straight to the point with no unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after creation (what gets returned, how to reference the new epic), doesn't address error conditions or validation rules, and provides no guidance on usage context despite multiple sibling creation tools. The 100% schema coverage helps with parameters but doesn't compensate for missing behavioral and contextual information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly with descriptions, titles, and enum-like explanations for priority and state. The description adds no parameter information beyond what's in the schema, meeting the baseline expectation but not providing additional semantic context about how parameters interact or affect the creation process.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create') and resource ('new epic in ServiceNow'), making the purpose immediately understandable. However, it doesn't differentiate this tool from similar creation tools like create_story, create_incident, or create_change_request, which would require explaining what distinguishes an epic from other ServiceNow entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With multiple creation tools available (create_story, create_incident, create_change_request, etc.), there's no indication of what scenarios warrant creating an epic specifically versus other entity types, nor any prerequisites or contextual requirements.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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