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ServiceNow MCP Server

get_workflow_details

Retrieve detailed information about a specific ServiceNow workflow, including its configuration and optional version history, to understand its structure and functionality.

Instructions

Get detailed information about a specific workflow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflow_idYesWorkflow ID or sys_id
include_versionsNoInclude workflow versions

Implementation Reference

  • Core implementation of the get_workflow_details tool. Fetches workflow details via ServiceNow REST API using the provided workflow_id.
    def get_workflow_details(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Get detailed information about a specific workflow.
        
        Args:
            auth_manager: Authentication manager
            server_config: Server configuration
            params: Parameters for getting workflow details
            
        Returns:
            Dictionary containing the workflow details
        """
        params = _unwrap_params(params, GetWorkflowDetailsParams)
        
        # 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"}
        
        # 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.get(url, headers=headers)
            response.raise_for_status()
            
            result = response.json()
            return {
                "workflow": result.get("result", {}),
            }
        except requests.RequestException as e:
            logger.error(f"Error getting workflow details: {e}")
            return {"error": str(e)}
        except Exception as e:
            logger.error(f"Unexpected error getting workflow details: {e}")
            return {"error": str(e)}
  • Pydantic model defining the input schema for the get_workflow_details tool, including required workflow_id.
    class GetWorkflowDetailsParams(BaseModel):
        """Parameters for getting workflow details."""
        
        workflow_id: str = Field(..., description="Workflow ID or sys_id")
        include_versions: Optional[bool] = Field(False, description="Include workflow versions")
  • Registration of the get_workflow_details tool in the central tool definitions dictionary used by the MCP server. Maps name to handler, schema, description, etc.
    "get_workflow_details": (
        get_workflow_details_tool,
        GetWorkflowDetailsParams,
        str,  # Expects JSON string
        "Get detailed information about a specific workflow",
        "json",  # Tool returns list/dict
    ),
  • Helper function used by get_workflow_details to unwrap and validate parameters using the Pydantic schema.
    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
  • Helper function used by get_workflow_details to normalize auth_manager and server_config arguments, handling potential 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?

No annotations are provided, so the description carries full burden. It states 'Get detailed information' but doesn't disclose what 'detailed' includes, whether it's a read-only operation, permission requirements, error conditions, or response format. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 redundancy. It's appropriately sized for a simple retrieval tool and front-loads the core action. Every word earns its place with zero waste.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'detailed information' includes, the response structure, or any behavioral traits like idempotency or error handling. For a tool in a context-rich server with many siblings, more guidance is needed to ensure correct usage.

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%, so the schema already documents both parameters (workflow_id, include_versions) thoroughly. The description adds no parameter-specific context beyond what's in the schema, such as explaining what 'detailed information' comprises or how include_versions affects output. Baseline 3 is appropriate when schema does the heavy lifting.

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 ('Get') and resource ('detailed information about a specific workflow'), making the purpose unambiguous. It distinguishes this from list-style tools like 'list_workflows' by focusing on a single workflow. However, it doesn't explicitly differentiate from 'get_workflow_activities' which also retrieves workflow-related details.

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. It doesn't mention sibling tools like 'list_workflows' (for browsing) or 'get_workflow_activities' (for activity-level details), nor does it specify prerequisites like needing a workflow ID. Usage context is implied but not articulated.

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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