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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,

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
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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