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JLKmach

ServiceNow MCP Server

by JLKmach

create_change_request

Create a new change request in ServiceNow to manage IT infrastructure modifications, specifying type, risk, impact, and schedule.

Instructions

Create a new change request in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
short_descriptionYesShort description of the change request
descriptionNoDetailed description of the change request
typeYesType of change (normal, standard, emergency)
riskNoRisk level of the change
impactNoImpact of the change
categoryNoCategory of the change
requested_byNoUser who requested the change
assignment_groupNoGroup assigned to the change
start_dateNoPlanned start date (YYYY-MM-DD HH:MM:SS)
end_dateNoPlanned end date (YYYY-MM-DD HH:MM:SS)

Implementation Reference

  • The core handler function that executes the create_change_request tool. It validates input params using Pydantic model, prepares data, and makes a POST request to ServiceNow's change_request table API.
    def create_change_request(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Create a new change request in ServiceNow.
    
        Args:
            auth_manager: The authentication manager.
            server_config: The server configuration.
            params: The parameters for creating the change request.
    
        Returns:
            The created change request.
        """
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            CreateChangeRequestParams, 
            required_fields=["short_description", "type"]
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # Prepare the request data
        data = {
            "short_description": validated_params.short_description,
            "type": validated_params.type,
        }
        
        # Add optional fields if provided
        if validated_params.description:
            data["description"] = validated_params.description
        if validated_params.risk:
            data["risk"] = validated_params.risk
        if validated_params.impact:
            data["impact"] = validated_params.impact
        if validated_params.category:
            data["category"] = validated_params.category
        if validated_params.requested_by:
            data["requested_by"] = validated_params.requested_by
        if validated_params.assignment_group:
            data["assignment_group"] = validated_params.assignment_group
        if validated_params.start_date:
            data["start_date"] = validated_params.start_date
        if validated_params.end_date:
            data["end_date"] = validated_params.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_request"
        
        try:
            response = requests.post(url, json=data, headers=headers)
            response.raise_for_status()
            
            result = response.json()
            
            return {
                "success": True,
                "message": "Change request created successfully",
                "change_request": result["result"],
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error creating change request: {e}")
            return {
                "success": False,
                "message": f"Error creating change request: {str(e)}",
            }
  • Pydantic BaseModel defining the input schema/validation for the create_change_request tool parameters.
    class CreateChangeRequestParams(BaseModel):
        """Parameters for creating a change request."""
    
        short_description: str = Field(..., description="Short description of the change request")
        description: Optional[str] = Field(None, description="Detailed description of the change request")
        type: str = Field(..., description="Type of change (normal, standard, emergency)")
        risk: Optional[str] = Field(None, description="Risk level of the change")
        impact: Optional[str] = Field(None, description="Impact of the change")
        category: Optional[str] = Field(None, description="Category of the change")
        requested_by: Optional[str] = Field(None, description="User who requested the change")
        assignment_group: Optional[str] = Field(None, description="Group assigned to the change")
        start_date: Optional[str] = Field(None, description="Planned start date (YYYY-MM-DD HH:MM:SS)")
        end_date: Optional[str] = Field(None, description="Planned end date (YYYY-MM-DD HH:MM:SS)")
  • Registration of the 'create_change_request' tool in the central tool_definitions dictionary used by the MCP server to expose the tool.
    "create_change_request": (
        create_change_request_tool,
        CreateChangeRequestParams,
        str,
        "Create a new change request in ServiceNow",
        "str",
    ),
  • Import of the create_change_request function into the tools package __init__, making it available for registration.
    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 the handler to validate and unwrap input parameters against the 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 the full burden of behavioral disclosure. While 'Create' implies a write operation, the description doesn't address critical behavioral aspects like required permissions, whether the creation triggers workflows or notifications, what happens on success/failure, or any rate limits. This leaves significant gaps for an agent to understand the tool's behavior.

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 states the core purpose without any wasted words. It's appropriately sized and front-loaded with the essential information.

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 creation tool with 10 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what happens after creation (e.g., returns an ID, triggers approval workflows), doesn't mention required permissions or constraints, and provides no context about the change request lifecycle. Given the complexity and lack of structured data, the description should do more to help an agent use this tool effectively.

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 input schema has 100% description coverage, with each parameter clearly documented in the schema itself. The tool description adds no additional parameter information beyond what's already in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.

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 verb ('Create') and resource ('a new change request in ServiceNow'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'update_change_request' or 'list_change_requests', which would require explicit comparison to achieve a score of 5.

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 sibling tools like 'update_change_request', 'list_change_requests', and 'approve_change' available, there's no indication of prerequisites, appropriate contexts, or when other tools might be more suitable.

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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