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ServiceNow MCP Server

submit_change_for_approval

Submit a change request for approval in ServiceNow by providing the change ID and optional comments. Streamline the approval process for IT changes.

Instructions

Submit a change request for approval

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Implementation Reference

  • The core handler function implementing the submit_change_for_approval tool. It validates parameters, updates the change request state to 'assess', adds work notes if provided, and creates an approval record in sysapproval_approver table via ServiceNow REST API.
    def submit_change_for_approval(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Submit a change request for approval in ServiceNow.
    
        Args:
            auth_manager: The authentication manager.
            server_config: The server configuration.
            params: The parameters for submitting a change request for approval.
    
        Returns:
            The result of the submission.
        """
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            SubmitChangeForApprovalParams,
            required_fields=["change_id"]
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # Prepare the request data
        data = {
            "state": "assess",  # Set state to "assess" to submit for approval
        }
        
        # Add approval comments if provided
        if validated_params.approval_comments:
            data["work_notes"] = validated_params.approval_comments
        
        # 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_request/{validated_params.change_id}"
        
        try:
            response = requests.patch(url, json=data, headers=headers)
            response.raise_for_status()
            
            # Now, create an approval request
            approval_url = f"{instance_url}/api/now/table/sysapproval_approver"
            approval_data = {
                "document_id": validated_params.change_id,
                "source_table": "change_request",
                "state": "requested",
            }
            
            approval_response = requests.post(approval_url, json=approval_data, headers=headers)
            approval_response.raise_for_status()
            
            approval_result = approval_response.json()
            
            return {
                "success": True,
                "message": "Change request submitted for approval successfully",
                "approval": approval_result["result"],
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error submitting change for approval: {e}")
            return {
                "success": False,
                "message": f"Error submitting change for approval: {str(e)}",
            }
  • Pydantic BaseModel defining the input schema for the tool: requires change_id, optional approval_comments.
    class SubmitChangeForApprovalParams(BaseModel):
        """Parameters for submitting a change request for approval."""
    
        change_id: str = Field(..., description="Change request ID or sys_id")
        approval_comments: Optional[str] = Field(None, description="Comments for the approval request")
  • Tool registration in get_tool_definitions() dictionary, mapping tool name to handler function alias, params schema, return type hint, description, and serialization method.
    "submit_change_for_approval": (
        submit_change_for_approval_tool,
        SubmitChangeForApprovalParams,
        str,
        "Submit a change request for approval",
        "str",  # Tool returns simple message
    ),
  • Import of the handler function aliased for use in tool registration.
        submit_change_for_approval as submit_change_for_approval_tool,
    )
  • Exposes the submit_change_for_approval function in the tools package __init__ for easy 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,
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. It implies a mutation (submitting for approval) but doesn't specify permissions required, whether it triggers notifications, if it's reversible, or what happens upon submission. This is a significant gap for a tool that likely alters workflow states.

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, clear sentence with no wasted words. It's front-loaded and efficiently conveys the core action, making it easy to parse quickly.

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?

Given the complexity of a submission tool in a workflow context, no annotations, no output schema, and minimal parameter coverage, the description is inadequate. It doesn't explain the approval process, expected outcomes, or error conditions, leaving the agent with insufficient information for reliable use.

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

Parameters4/5

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

The description doesn't mention parameters, but with only 1 required parameter (change_id) and 0% schema description coverage, it's minimal. Since there's only one essential parameter, the lack of param details in the description is less critical, but it doesn't add value beyond the schema.

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

Purpose3/5

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

The description 'Submit a change request for approval' clearly states the action (submit) and target (change request for approval), which is adequate. However, it doesn't differentiate from sibling tools like 'approve_change' or 'reject_change', leaving ambiguity about its specific role in the approval workflow.

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. It doesn't mention prerequisites (e.g., if the change request must be in a draft state), exclusions, or how it relates to siblings like 'approve_change' or 'reject_change', leaving the agent to infer usage context.

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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