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ServiceNow MCP Server

by javerthl

update_workflow

Modify an existing ServiceNow workflow by updating its name, description, table assignment, active status, or additional attributes using the workflow ID.

Instructions

Update an existing workflow in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
activeNoWhether the workflow is active
attributesNoAdditional attributes for the workflow
descriptionNoDescription of the workflow
nameNoName of the workflow
tableNoTable the workflow applies to
workflow_idYesWorkflow ID or sys_id

Implementation Reference

  • The main handler function that performs the PATCH request to update a workflow in ServiceNow using the provided parameters.
    def update_workflow(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Update an existing workflow in ServiceNow.
        
        Args:
            auth_manager: Authentication manager
            server_config: Server configuration
            params: Parameters for updating a workflow
            
        Returns:
            Dict[str, Any]: Updated workflow details
        """
        # Unwrap parameters if needed
        params = _unwrap_params(params, UpdateWorkflowParams)
        
        # 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)}
        
        workflow_id = params.get("workflow_id")
        if not workflow_id:
            return {"error": "Workflow ID is required"}
        
        # Prepare data for the API request
        data = {}
        
        if params.get("name"):
            data["name"] = params["name"]
        
        if params.get("description") is not None:
            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"])
        
        if not data:
            return {"error": "No update parameters provided"}
        
        # Make the API request
        try:
            headers = auth_manager.get_headers()
            url = f"{server_config.instance_url}/api/now/table/wf_workflow/{workflow_id}"
            
            response = requests.patch(url, headers=headers, json=data)
            response.raise_for_status()
            
            result = response.json()
            return {
                "workflow": result.get("result", {}),
                "message": "Workflow updated successfully",
            }
        except requests.RequestException as e:
            logger.error(f"Error updating workflow: {e}")
            return {"error": str(e)}
        except Exception as e:
            logger.error(f"Unexpected error updating workflow: {e}")
            return {"error": str(e)}
  • Pydantic model defining the input parameters for the update_workflow tool, including workflow_id (required) and optional fields like name, description, table, active, and attributes.
    class UpdateWorkflowParams(BaseModel):
        """Parameters for updating a workflow."""
        
        workflow_id: str = Field(..., description="Workflow ID or sys_id")
        name: Optional[str] = Field(None, 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(None, description="Whether the workflow is active")
        attributes: Optional[Dict[str, Any]] = Field(None, description="Additional attributes for the workflow")
  • MCP tool registration entry in get_tool_definitions dictionary, specifying the implementation function (update_workflow_tool alias), input schema model (UpdateWorkflowParams), return type hint, description, and serialization method.
    "update_workflow": (
        update_workflow_tool,
        UpdateWorkflowParams,
        str,  # Expects JSON string
        "Update an existing workflow in ServiceNow",
        "json_dict",  # Tool returns Pydantic model
    ),
  • Import statement in tools/__init__.py that exposes update_workflow for use in tool_utils.py and elsewhere.
    from servicenow_mcp.tools.workflow_tools import (
        activate_workflow,
        add_workflow_activity,
        create_workflow,
        deactivate_workflow,
        delete_workflow_activity,
        get_workflow_activities,
        get_workflow_details,
        list_workflow_versions,
        list_workflows,
        reorder_workflow_activities,
        update_workflow,
        update_workflow_activity,
    )
  • Helper function used in update_workflow to unwrap and validate parameters using the Pydantic model.
    def _unwrap_params(params: Any, param_class: Type[T]) -> Dict[str, Any]:
        """
        Unwrap parameters if they're wrapped in a Pydantic model.
        This helps handle cases where the parameters are passed as a model instead of a dict.
        """
        if isinstance(params, dict):
            return params
        if isinstance(params, param_class):
            return params.dict(exclude_none=True)
        return params
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 'Update' implies a mutation operation, the description doesn't address critical behavioral aspects: what permissions are required, whether the update is reversible, what happens when only partial parameters are provided, or what the response format looks like. For a mutation tool with zero annotation coverage, this represents a significant gap in transparency.

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 gets straight to the point with zero wasted words. It's appropriately sized for a basic tool description and front-loads the essential information. Every word earns its place in this minimal but complete statement of function.

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?

Given that this is a mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address what the tool returns, what error conditions might occur, or important behavioral constraints. For a tool that modifies workflows in a complex system like ServiceNow, more contextual information about the operation's scope and limitations would be expected.

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%, meaning all 6 parameters are documented in the input schema. The description adds no additional parameter information beyond what's already in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline score is 3 even with no parameter information 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 action ('Update') and resource ('an existing workflow in ServiceNow'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'update_workflow_activity' or 'create_workflow', which would require more specificity about what distinguishes this particular workflow update operation.

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 'create_workflow', 'activate_workflow', 'deactivate_workflow', and 'update_workflow_activity', there's no indication of when this general update tool is appropriate versus more specialized operations. No prerequisites or exclusions are mentioned.

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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