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vparlapalli490

ServiceNow MCP Server

create_workflow

Create a new workflow in ServiceNow to automate business processes by specifying name, description, target table, and activation status.

Instructions

Create a new workflow in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName of the workflow
descriptionNoDescription of the workflow
tableNoTable the workflow applies to
activeNoWhether the workflow is active
attributesNoAdditional attributes for the workflow

Implementation Reference

  • The core handler function for the 'create_workflow' MCP tool. It validates parameters, prepares the payload, and sends a POST request to the ServiceNow 'wf_workflow' table API to create a new workflow.
    def create_workflow(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Create a new workflow in ServiceNow.
        
        Args:
            auth_manager: Authentication manager
            server_config: Server configuration
            params: Parameters for creating a workflow
            
        Returns:
            Dict[str, Any]: Created workflow details
        """
        # Unwrap parameters if needed
        params = _unwrap_params(params, CreateWorkflowParams)
        
        # Get the correct auth_manager and server_config
        try:
            auth_manager, server_config = _get_auth_and_config(auth_manager, server_config)
        except ValueError as e:
            logger.error(f"Error getting auth and config: {e}")
            return {"error": str(e)}
        
        # Validate required parameters
        if not params.get("name"):
            return {"error": "Workflow name is required"}
        
        # Prepare data for the API request
        data = {
            "name": params["name"],
        }
        
        if params.get("description"):
            data["description"] = params["description"]
        
        if params.get("table"):
            data["table"] = params["table"]
        
        if params.get("active") is not None:
            data["active"] = str(params["active"]).lower()
        
        if params.get("attributes"):
            # Add any additional attributes
            data.update(params["attributes"])
        
        # Make the API request
        try:
            headers = auth_manager.get_headers()
            url = f"{server_config.instance_url}/api/now/table/wf_workflow"
            
            response = requests.post(url, headers=headers, json=data)
            response.raise_for_status()
            
            result = response.json()
            return {
                "workflow": result.get("result", {}),
                "message": "Workflow created successfully",
            }
        except requests.RequestException as e:
            logger.error(f"Error creating workflow: {e}")
            return {"error": str(e)}
        except Exception as e:
            logger.error(f"Unexpected error creating workflow: {e}")
            return {"error": str(e)}
  • Pydantic BaseModel defining the input schema (parameters) for the create_workflow tool, including required name and optional fields like description, table, active status, and attributes.
    class CreateWorkflowParams(BaseModel):
        """Parameters for creating a new workflow."""
        
        name: str = Field(..., description="Name of the workflow")
        description: Optional[str] = Field(None, description="Description of the workflow")
        table: Optional[str] = Field(None, description="Table the workflow applies to")
        active: Optional[bool] = Field(True, description="Whether the workflow is active")
        attributes: Optional[Dict[str, Any]] = Field(None, description="Additional attributes for the workflow")
  • Registers the 'create_workflow' tool in the central tool definitions dictionary returned by get_tool_definitions(). Specifies the aliased handler function, input params model, return type hint, description, and serialization method for MCP server integration.
    "create_workflow": (
        create_workflow_tool,
        CreateWorkflowParams,
        str,  # Expects JSON string
        "Create a new workflow in ServiceNow",
        "json_dict",  # Tool returns Pydantic model
  • Helper function used by create_workflow (and other tools) to flexibly resolve AuthManager and ServerConfig instances, handling potential parameter order swaps.
    def _get_auth_and_config(
        auth_manager_or_config: Union[AuthManager, ServerConfig],
        server_config_or_auth: Union[ServerConfig, AuthManager],
    ) -> tuple[AuthManager, ServerConfig]:
        """
        Get the correct auth_manager and server_config objects.
        
        This function handles the case where the parameters might be swapped.
        
        Args:
            auth_manager_or_config: Either an AuthManager or a ServerConfig.
            server_config_or_auth: Either a ServerConfig or an AuthManager.
            
        Returns:
            tuple[AuthManager, ServerConfig]: The correct auth_manager and server_config.
            
        Raises:
            ValueError: If the parameters are not of the expected types.
        """
        # Check if the parameters are in the correct order
        if isinstance(auth_manager_or_config, AuthManager) and isinstance(server_config_or_auth, ServerConfig):
            return auth_manager_or_config, server_config_or_auth
        
        # Check if the parameters are swapped
        if isinstance(auth_manager_or_config, ServerConfig) and isinstance(server_config_or_auth, AuthManager):
            return server_config_or_auth, auth_manager_or_config
        
        # If we get here, at least one of the parameters is not of the expected type
        if hasattr(auth_manager_or_config, "get_headers"):
            auth_manager = auth_manager_or_config
        elif hasattr(server_config_or_auth, "get_headers"):
            auth_manager = server_config_or_auth
        else:
            raise ValueError("Cannot find get_headers method in either auth_manager or server_config")
        
        if hasattr(auth_manager_or_config, "instance_url"):
            server_config = auth_manager_or_config
        elif hasattr(server_config_or_auth, "instance_url"):
            server_config = server_config_or_auth
        else:
            raise ValueError("Cannot find instance_url attribute in either auth_manager or server_config")
        
        return auth_manager, server_config
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 'Create' implies a write operation, the description doesn't mention required permissions, whether the workflow becomes active by default, what happens on success/failure, or any side effects. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding its 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 for a basic creation tool and gets straight to the point. Every word earns its place in conveying the essential action.

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 no annotations and no output schema, the description is insufficiently complete. It doesn't explain what constitutes a successful creation, what gets returned, or any behavioral nuances. Given the complexity of workflow creation in ServiceNow and the lack of structured behavioral information, the description should provide more context about the operation.

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 parameters well-documented in the input schema. The description adds no additional parameter information beyond what's already in the structured schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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 ('new workflow in ServiceNow'), making the purpose immediately understandable. It doesn't specifically differentiate from sibling tools like 'update_workflow' or 'activate_workflow', but the creation action is unambiguous. The description avoids tautology by not just repeating the tool name.

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 like 'update_workflow' or 'activate_workflow'. There's no mention of prerequisites, constraints, or typical use cases. The agent must infer usage from the tool name alone, which is insufficient for optimal selection.

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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