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JLKmach

ServiceNow MCP Server

by JLKmach

add_change_task

Add tasks to ServiceNow change requests to track implementation steps, assign responsibilities, and schedule activities for controlled IT changes.

Instructions

Add a task to a change request

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
change_idYesChange request ID or sys_id
short_descriptionYesShort description of the task
descriptionNoDetailed description of the task
assigned_toNoUser assigned to the task
planned_start_dateNoPlanned start date (YYYY-MM-DD HH:MM:SS)
planned_end_dateNoPlanned end date (YYYY-MM-DD HH:MM:SS)

Implementation Reference

  • The core handler function implementing the logic to add a change task to a ServiceNow change request, including validation, API call, and error handling.
    def add_change_task(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Add a task to a change request in ServiceNow.
    
        Args:
            auth_manager: The authentication manager.
            server_config: The server configuration.
            params: The parameters for adding a change task.
    
        Returns:
            The created change task.
        """
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            AddChangeTaskParams,
            required_fields=["change_id", "short_description"]
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # Prepare the request data
        data = {
            "change_request": validated_params.change_id,
            "short_description": validated_params.short_description,
        }
        
        # Add optional fields if provided
        if validated_params.description:
            data["description"] = validated_params.description
        if validated_params.assigned_to:
            data["assigned_to"] = validated_params.assigned_to
        if validated_params.planned_start_date:
            data["planned_start_date"] = validated_params.planned_start_date
        if validated_params.planned_end_date:
            data["planned_end_date"] = validated_params.planned_end_date
        
        # 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/change_task"
        
        try:
            response = requests.post(url, json=data, headers=headers)
            response.raise_for_status()
            
            result = response.json()
            
            return {
                "success": True,
                "message": "Change task added successfully",
                "change_task": result["result"],
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error adding change task: {e}")
            return {
                "success": False,
                "message": f"Error adding change task: {str(e)}",
            }
  • Pydantic BaseModel defining the input schema and validation for the add_change_task tool parameters.
    class AddChangeTaskParams(BaseModel):
        """Parameters for adding a task to a change request."""
    
        change_id: str = Field(..., description="Change request ID or sys_id")
        short_description: str = Field(..., description="Short description of the task")
        description: Optional[str] = Field(None, description="Detailed description of the task")
        assigned_to: Optional[str] = Field(None, description="User assigned to the task")
        planned_start_date: Optional[str] = Field(None, description="Planned start date (YYYY-MM-DD HH:MM:SS)")
        planned_end_date: Optional[str] = Field(None, description="Planned end date (YYYY-MM-DD HH:MM:SS)")
  • MCP tool registration in the central tool_definitions dictionary, associating the tool name with its handler function, input schema, description, and serialization settings.
    "add_change_task": (
        add_change_task_tool,
        AddChangeTaskParams,
        str,  # Expects JSON string
        "Add a task to a change request",
        "json_dict",  # Tool returns Pydantic model
    ),
  • Shared helper function called by the handler to unwrap, validate required fields, and parse input parameters using the Pydantic 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)}",
            }
  • Re-export of the add_change_task handler from change_tools.py in the tools package __init__, making it available for import.
    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,
    )

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure but provides minimal information. It states this is an 'add' operation (implying creation/mutation) but doesn't mention permissions required, whether this triggers notifications or workflows, what happens on success/failure, or any rate limits. For a mutation tool with zero annotation coverage, this is inadequate.

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 directly states the tool's purpose with zero wasted words. It's appropriately sized for a straightforward tool and front-loads the essential information immediately.

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 insufficiently complete. It doesn't explain what happens after adding the task (success indicators, return values, error conditions), doesn't mention prerequisites or permissions, and provides no behavioral context. The 100% schema coverage helps with parameters but doesn't compensate for the overall behavioral gap.

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 6 parameters thoroughly. The description adds no parameter information beyond what's in the schema, providing zero additional semantic context about parameters like 'change_id' or 'short_description'. Baseline 3 is appropriate when schema does all the work.

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 ('Add a task') and target resource ('to a change request'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'add_workflow_activity' or 'add_comment' that also add items to other entities, missing explicit sibling differentiation.

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 many sibling tools available (like 'add_workflow_activity' or 'add_comment'), there's no indication of when this specific task-adding operation is appropriate versus other addition operations in the system.

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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