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JLKmach

ServiceNow MCP Server

by JLKmach

create_workflow

Create new workflows in ServiceNow to automate business processes, define rules, and manage task sequences for incident management, change requests, or service operations.

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

  • Main execution function for the create_workflow tool. Handles parameter unwrapping, API call to ServiceNow wf_workflow table, and returns the created workflow details.
    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 model defining the input parameters for the create_workflow tool, including name, 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")
  • Tool registration in get_tool_definitions dictionary, associating the handler function, schema, description, and serialization method for MCP server.
    "create_workflow": (
        create_workflow_tool,
        CreateWorkflowParams,
        str,  # Expects JSON string
        "Create a new workflow in ServiceNow",
        "json_dict",  # Tool returns Pydantic model
    ),
  • Import of create_workflow function in tools package __init__.py, exposing it for use.
    from servicenow_mcp.tools.workflow_tools import (
        activate_workflow,
        add_workflow_activity,
        create_workflow,
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Create' implies a write/mutation operation, the description doesn't address permissions needed, whether the workflow is active by default, what happens on duplicate names, or the format of the response. This leaves significant gaps for a mutation tool.

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, with every word earning its place in conveying the core functionality.

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 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what constitutes a successful creation, what values are returned, or behavioral aspects like default activation state. The 100% schema coverage helps with parameters but doesn't compensate for the lack of operational context.

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?

Schema description coverage is 100%, with all parameters well-documented in the schema itself. The description adds no additional parameter information beyond what's already in the structured fields, so it meets the baseline for adequate but not exceptional parameter documentation.

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 ('Create') and resource ('new workflow in ServiceNow'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'update_workflow' or 'list_workflows', which would require explicit comparison to achieve a perfect 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. With sibling tools like 'update_workflow', 'list_workflows', and 'activate_workflow' available, there's no indication of prerequisites, appropriate contexts, or exclusions for this creation operation.

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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