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ServiceNow MCP Server

by javerthl

add_change_task

Add a task to a ServiceNow change request by specifying the change ID, task description, and optional details like assignment and dates.

Instructions

Add a task to a change request

Input Schema

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

Implementation Reference

  • Main implementation of the add_change_task tool handler. Handles parameter validation using AddChangeTaskParams, constructs the API request to create a change_task in ServiceNow's change_task table, and returns the result.
    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 model defining the input parameters for the add_change_task tool, including required change_id and short_description, and optional fields.
    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)")
  • Registration of the add_change_task tool in the central tool definitions dictionary used by the MCP server. Maps the tool name to its handler (aliased import), 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
    ),
  • Import of add_change_task from change_tools.py into the tools package __init__.py, making it available for re-export and use in tool_utils.py.
    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,
    )
  • Helper function used by add_change_task (and other tools) for parameter unwrapping, required field checks, and Pydantic validation.
    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?

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Add a task' implies a write/mutation operation, the description doesn't disclose any behavioral traits: no information about permissions required, whether this is an idempotent operation, what happens on failure, or what the response looks like. For a mutation tool with zero annotation coverage, this is a significant gap.

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 extremely concise at just 6 words, with zero wasted language. It's front-loaded with the core action and target, making it immediately understandable. Every word earns its place in this minimal description.

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 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't address the behavioral implications of adding tasks, doesn't explain the relationship to change requests, and provides no information about what happens after invocation. The description should do more to compensate for the lack of structured metadata.

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?

The schema description coverage is 100%, with all 6 parameters well-documented in the input schema. The description adds no parameter information beyond what's already in the schema, so it meets the baseline of 3 for high schema coverage. No additional parameter semantics are provided in the description text.

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 distinguish this tool from similar sibling tools like 'create_scrum_task' or 'add_workflow_activity', which also create task-like entities in different contexts.

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 that create or add various entities (tasks, activities, dependencies), there's no indication of the specific context for change request tasks versus other task types or when this tool is appropriate versus similar operations.

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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