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javerthl

ServiceNow MCP Server

by javerthl

list_workflows

Retrieve and filter workflows from ServiceNow to manage automation processes, with options to limit results, apply status filters, and search by name.

Instructions

List workflows from ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
activeNoFilter by active status
limitNoMaximum number of records to return
nameNoFilter by name (contains)
offsetNoOffset to start from
queryNoAdditional query string

Implementation Reference

  • The handler function that executes the list_workflows tool: unwraps parameters using the schema, builds ServiceNow query, calls REST API on wf_workflow table, returns workflows list.
    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 BaseModel defining input schema/parameters for the list_workflows tool.
    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 tuple in get_tool_definitions(): maps 'list_workflows' to its handler (list_workflows_tool), input schema (ListWorkflowsParams), description, and serialization hint. Used by server.py to expose the tool via MCP.
    "list_workflows": (
        list_workflows_tool,
        ListWorkflowsParams,
        str,  # Expects JSON string
        "List workflows from ServiceNow",
        "json",  # Tool returns list/dict
    ),
  • Helper function to handle potentially swapped auth_manager and server_config arguments passed to tools.
    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 to unwrap Pydantic model params to dict, used 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
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 but only states the basic action without details on permissions, rate limits, pagination, or response format. It doesn't clarify if this is a read-only operation, what data is returned, or any constraints, leaving significant gaps for an agent to understand how the tool behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no wasted words, making it efficient and easy to parse. However, it's overly brief and could benefit from more detail to improve utility without sacrificing conciseness, as it currently under-specifies the tool's scope and behavior.

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 the lack of annotations and output schema, the description is incomplete for a tool with 5 parameters and no behavioral context. It doesn't explain what 'list' returns (e.g., workflow names, IDs, metadata), how results are structured, or any operational limits, making it inadequate for an agent to use the tool effectively without additional assumptions.

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%, with all 5 parameters well-documented in the schema (e.g., 'active' for filtering by status, 'limit' for maximum records). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for adequate but not enhanced coverage.

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) with the source (ServiceNow), making the purpose understandable. However, it lacks specificity about what 'list' entails (e.g., retrieving metadata, filtering capabilities) and doesn't distinguish it from sibling tools like 'get_workflow_details' or 'list_workflow_versions', leaving room for ambiguity.

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 'get_workflow_details' for specific workflows or 'list_workflow_versions' for version history, nor does it specify prerequisites or contexts where listing workflows is appropriate, such as for overviews or bulk operations.

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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