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vparlapalli490

ServiceNow MCP Server

get_change_request_details

Retrieve comprehensive details for a specific change request in ServiceNow by providing its ID or sys_id.

Instructions

Get detailed information about a specific change request

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
change_idYesChange request ID or sys_id

Implementation Reference

  • The handler function implementing the 'get_change_request_details' tool. It validates parameters using GetChangeRequestDetailsParams, fetches the change request details from the ServiceNow API, retrieves associated change tasks, and returns both.
    def get_change_request_details(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Get details of a change request from ServiceNow.
    
        Args:
            auth_manager: The authentication manager.
            server_config: The server configuration.
            params: The parameters for getting change request details.
    
        Returns:
            The change request details.
        """
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            GetChangeRequestDetailsParams,
            required_fields=["change_id"]
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # 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",
            }
        
        # Make the API request
        url = f"{instance_url}/api/now/table/change_request/{validated_params.change_id}"
        
        params = {
            "sysparm_display_value": "true",
        }
        
        try:
            response = requests.get(url, headers=headers, params=params)
            response.raise_for_status()
            
            result = response.json()
            
            # Get tasks associated with this change request
            tasks_url = f"{instance_url}/api/now/table/change_task"
            tasks_params = {
                "sysparm_query": f"change_request={validated_params.change_id}",
                "sysparm_display_value": "true",
            }
            
            tasks_response = requests.get(tasks_url, headers=headers, params=tasks_params)
            tasks_response.raise_for_status()
            
            tasks_result = tasks_response.json()
            
            return {
                "success": True,
                "change_request": result["result"],
                "tasks": tasks_result["result"],
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error getting change request details: {e}")
            return {
                "success": False,
                "message": f"Error getting change request details: {str(e)}",
            }
  • Pydantic model defining the input parameters for the get_change_request_details tool, requiring a 'change_id'.
    class GetChangeRequestDetailsParams(BaseModel):
        """Parameters for getting change request details."""
    
        change_id: str = Field(..., description="Change request ID or sys_id")
  • Registration of the 'get_change_request_details' tool in the central tool_definitions dictionary, specifying the handler function alias, input schema, return type hint, description, and serialization method.
    "get_change_request_details": (
        get_change_request_details_tool,
        GetChangeRequestDetailsParams,
        str,  # Expects JSON string
        "Get detailed information about a specific change request",
        "json",  # Tool returns list/dict
    ),
  • Re-export of the get_change_request_details function from change_tools.py in the tools package __init__.
    get_change_request_details,
  • Helper function used by the handler to unwrap, validate parameters against the schema, and handle various input formats.
    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. It states this is a read operation ('Get'), implying it's likely non-destructive, but doesn't specify permissions, rate limits, error conditions, or the format of returned details. This is a significant gap for a tool with no annotation coverage.

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 without unnecessary words. It's appropriately sized and front-loaded, making it easy to understand at a glance.

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

Completeness3/5

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

Given the tool's low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks behavioral details and usage context, which are important for effective tool selection and invocation by an AI agent.

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 the single parameter 'change_id' documented as 'Change request ID or sys_id'. The description doesn't add any meaning beyond this, such as examples or constraints, so it meets the baseline for high schema coverage without compensating value.

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 ('Get') and resource ('detailed information about a specific change request'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'get_changeset_details' or 'list_change_requests' which might retrieve similar information, so it doesn't reach the highest score.

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 like needing a specific change ID, nor does it compare to sibling tools such as 'list_change_requests' for broader queries or 'get_changeset_details' for related data, leaving usage context unclear.

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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