Skip to main content
Glama
echelon-ai-labs

ServiceNow MCP Server

list_workflow_versions

Retrieve and list versions of a specific workflow in ServiceNow using a defined workflow ID, with options to set limits and offsets for returned records.

Instructions

List workflow versions from ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Implementation Reference

  • The handler function that executes the tool: queries ServiceNow API for wf_workflow_version table filtered by workflow_id, handles pagination and errors.
    def list_workflow_versions(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        List versions of a specific workflow.
        
        Args:
            auth_manager: Authentication manager
            server_config: Server configuration
            params: Parameters for listing workflow versions
            
        Returns:
            Dict[str, Any]: List of workflow versions
        """
        # Unwrap parameters if needed
        params = _unwrap_params(params, ListWorkflowVersionsParams)
        
        # 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"}
        
        # Convert parameters to ServiceNow query format
        query_params = {
            "sysparm_query": f"workflow={workflow_id}",
            "sysparm_limit": params.get("limit", 10),
            "sysparm_offset": params.get("offset", 0),
        }
        
        # Make the API request
        try:
            headers = auth_manager.get_headers()
            url = f"{server_config.instance_url}/api/now/table/wf_workflow_version"
            
            response = requests.get(url, headers=headers, params=query_params)
            response.raise_for_status()
            
            result = response.json()
            return {
                "versions": result.get("result", []),
                "count": len(result.get("result", [])),
                "total": int(response.headers.get("X-Total-Count", 0)),
                "workflow_id": workflow_id,
            }
        except requests.RequestException as e:
            logger.error(f"Error listing workflow versions: {e}")
            return {"error": str(e)}
        except Exception as e:
            logger.error(f"Unexpected error listing workflow versions: {e}")
            return {"error": str(e)}
  • Pydantic BaseModel for input parameters validation: requires workflow_id, optional limit and offset.
    class ListWorkflowVersionsParams(BaseModel):
        """Parameters for listing workflow versions."""
        
        workflow_id: str = Field(..., description="Workflow ID or sys_id")
        limit: Optional[int] = Field(10, description="Maximum number of records to return")
        offset: Optional[int] = Field(0, description="Offset to start from")
  • Tool registration entry in get_tool_definitions(): maps name to handler (aliased import), schema, return type hint, description, and JSON serialization method.
    "list_workflow_versions": (
        list_workflow_versions_tool,
        ListWorkflowVersionsParams,
        str,  # Expects JSON string
        "List workflow versions from ServiceNow",
        "json",  # Tool returns list/dict
    ),
  • Helper function to normalize input params to dict, unwrapping Pydantic models if needed; used at line 317 in handler.
    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 to correctly order and extract AuthManager and ServerConfig from arguments, handling possible swaps; used in handler.
    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?

With no annotations provided, the description carries full burden but only states the basic action without disclosing behavioral traits like pagination, rate limits, permissions required, or response format. It doesn't mention that it's a read-only operation or any constraints, leaving significant gaps for a tool with parameters.

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 zero waste, front-loading the core action. It's appropriately sized for such a simple statement, though this conciseness comes at the cost of detail.

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, no output schema, and low schema coverage, the description is incomplete. It doesn't cover parameter meanings, behavioral aspects, or usage context, making it inadequate for a tool with parameters and siblings in a complex server environment.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description provides no information about parameters beyond what the schema includes. It doesn't explain the meaning of 'workflow_id', 'limit', or 'offset', or how they affect the listing, failing to compensate for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'List workflow versions from ServiceNow' clearly states the action (list) and resource (workflow versions), but it's vague about scope and doesn't distinguish from siblings like 'list_workflows' or 'get_workflow_details'. It doesn't specify whether this lists all versions or filtered ones, making it minimally adequate but with gaps.

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 'list_workflows' or 'get_workflow_details'. The description lacks context about prerequisites, filtering, or typical use cases, offering no help in tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/echelon-ai-labs/servicenow-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server