Skip to main content
Glama
echelon-ai-labs

ServiceNow MCP Server

list_workflows

Retrieve and filter workflows from ServiceNow instances using customizable parameters like active status, name, and query limits for efficient workflow management.

Instructions

List workflows from ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Implementation Reference

  • The handler function that executes the list_workflows tool logic. It unwraps parameters, handles auth/config ordering, builds ServiceNow query parameters, makes a GET request to /api/now/table/wf_workflow, and returns the workflows list with count and total.
    def list_workflows(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        List workflows from ServiceNow.
        
        Args:
            auth_manager: Authentication manager
            server_config: Server configuration
            params: Parameters for listing workflows
            
        Returns:
            Dictionary containing the list of workflows
        """
        params = _unwrap_params(params, ListWorkflowsParams)
        
        # 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)}
        
        # Convert parameters to ServiceNow query format
        query_params = {
            "sysparm_limit": params.get("limit", 10),
            "sysparm_offset": params.get("offset", 0),
        }
        
        # Build query string
        query_parts = []
        
        if params.get("active") is not None:
            query_parts.append(f"active={str(params['active']).lower()}")
        
        if params.get("name"):
            query_parts.append(f"nameLIKE{params['name']}")
        
        if params.get("query"):
            query_parts.append(params["query"])
        
        if query_parts:
            query_params["sysparm_query"] = "^".join(query_parts)
        
        # Make the API request
        try:
            headers = auth_manager.get_headers()
            url = f"{server_config.instance_url}/api/now/table/wf_workflow"
            
            response = requests.get(url, headers=headers, params=query_params)
            response.raise_for_status()
            
            result = response.json()
            return {
                "workflows": result.get("result", []),
                "count": len(result.get("result", [])),
                "total": int(response.headers.get("X-Total-Count", 0)),
            }
        except requests.RequestException as e:
            logger.error(f"Error listing workflows: {e}")
            return {"error": str(e)}
        except Exception as e:
            logger.error(f"Unexpected error listing workflows: {e}")
            return {"error": str(e)}
  • Pydantic model defining the input schema for the list_workflows tool, including optional parameters for pagination, filtering by active status, name, and custom query.
    class ListWorkflowsParams(BaseModel):
        """Parameters for listing workflows."""
        
        limit: Optional[int] = Field(10, description="Maximum number of records to return")
        offset: Optional[int] = Field(0, description="Offset to start from")
        active: Optional[bool] = Field(None, description="Filter by active status")
        name: Optional[str] = Field(None, description="Filter by name (contains)")
        query: Optional[str] = Field(None, description="Additional query string")
  • Tool registration entry in get_tool_definitions() dictionary, associating 'list_workflows' with its implementation function (list_workflows_tool), input schema (ListWorkflowsParams), return type hint, description, and serialization method.
    "list_workflows": (
        list_workflows_tool,
        ListWorkflowsParams,
        str,  # Expects JSON string
        "List workflows from ServiceNow",
        "json",  # Tool returns list/dict
    ),
  • Helper function used by list_workflows (and other tools) to flexibly resolve AuthManager and ServerConfig arguments, handling potential order swapping.
    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
  • Helper function to unwrap tool parameters from Pydantic model to dict if necessary.
    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 full burden but only states the action without behavioral details. It doesn't disclose whether this is a read-only operation, what permissions might be required, if there are rate limits, pagination behavior (implied by limit/offset params but not described), or what the output format looks like. For a list operation with multiple parameters, this is inadequate.

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 extremely concise at just 4 words, with no wasted language. It's front-loaded with the core action and resource. While it lacks detail, every word serves a purpose in stating the basic 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?

For a list operation with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what a 'workflow' entails in ServiceNow context, how results are structured, or provide any behavioral context. The agent must rely entirely on the parameter names and default values in the schema without semantic guidance.

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?

The description provides zero information about parameters, while the schema has 0% description coverage (the schema's descriptions are in the properties, not the overall schema). With 5 parameters (active, limit, name, offset, query) completely undocumented in the description, and no context about how filtering works (e.g., 'name' uses contains matching), this fails to compensate for the schema coverage gap.

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 workflows from ServiceNow' states the basic action (list) and resource (workflows) but lacks specificity about scope or format. It doesn't differentiate from sibling tools like 'list_workflow_versions' or 'get_workflow_details', leaving the agent to infer differences. The purpose is clear but vague about what exactly is being listed.

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 like 'get_workflow_details' for specific workflows or 'list_workflow_versions' for version listings. The description offers no context about prerequisites, typical use cases, or exclusions, leaving the agent to guess based on tool names alone.

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