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ServiceNow MCP Server

by javerthl

deactivate_workflow

Deactivate a ServiceNow workflow by providing its workflow ID or sys_id to stop automated processes and prevent workflow execution.

Instructions

Deactivate a workflow in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflow_idYesWorkflow ID or sys_id

Implementation Reference

  • The main handler function that implements the deactivate_workflow tool. It deactivates a workflow by patching the active field to false via the ServiceNow API.
    def deactivate_workflow(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Deactivate a workflow in ServiceNow.
        
        Args:
            auth_manager: Authentication manager
            server_config: Server configuration
            params: Parameters for deactivating a workflow
            
        Returns:
            Dict[str, Any]: Deactivated workflow details
        """
        # Unwrap parameters if needed
        params = _unwrap_params(params, DeactivateWorkflowParams)
        
        # 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 = {
            "active": "false",
        }
        
        # 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 deactivated successfully",
            }
        except requests.RequestException as e:
            logger.error(f"Error deactivating workflow: {e}")
            return {"error": str(e)}
        except Exception as e:
            logger.error(f"Unexpected error deactivating workflow: {e}")
            return {"error": str(e)}
  • Pydantic model defining the input parameters for the deactivate_workflow tool, requiring workflow_id.
    class DeactivateWorkflowParams(BaseModel):
        """Parameters for deactivating a workflow."""
        
        workflow_id: str = Field(..., description="Workflow ID or sys_id")
  • Registration of the deactivate_workflow tool in the central tool definitions dictionary, mapping name to function, params model, return type, description, and serialization method.
    "deactivate_workflow": (
        deactivate_workflow_tool,
        DeactivateWorkflowParams,
        str,
        "Deactivate a workflow in ServiceNow",
        "str",  # Tool returns simple message
    ),
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. It mentions 'Deactivate' which implies a state change, but doesn't specify if this is reversible, requires permissions, affects dependent processes, or what the outcome looks like, leaving critical behavioral traits unclear.

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, direct sentence with no wasted words, efficiently conveying the core action and context. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 no annotations and no output schema, the description is incomplete. It lacks details on behavioral implications, success/failure responses, and usage context, leaving gaps that could hinder an AI agent's ability to invoke it correctly in complex scenarios.

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, clearly documenting the 'workflow_id' parameter. The description adds no additional parameter information beyond what the schema provides, 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 action ('Deactivate') and resource ('a workflow in ServiceNow'), making the purpose specific and understandable. However, it doesn't differentiate from its sibling 'activate_workflow' beyond the opposite action, missing explicit comparison.

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?

No guidance is provided on when to use this tool versus alternatives, such as 'delete_workflow' or 'update_workflow', or prerequisites like workflow state. The description only states what it does, not when it's appropriate.

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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