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JLKmach

ServiceNow MCP Server

by JLKmach

list_stories

Retrieve and filter user stories from ServiceNow to manage agile project workflows. Use parameters like state, timeframe, and assignment group to find specific stories.

Instructions

List stories from ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of records to return
offsetNoOffset to start from
stateNoFilter by state
assignment_groupNoFilter by assignment group
timeframeNoFilter by timeframe (upcoming, in-progress, completed)
queryNoAdditional query string

Implementation Reference

  • The handler function that implements the list_stories tool logic, querying the ServiceNow rm_story table via REST API with filtering and pagination.
    def list_stories(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        List stories from ServiceNow.
    
        Args:
            auth_manager: The authentication manager.
            server_config: The server configuration.
            params: The parameters for listing stories.
    
        Returns:
            A list of stories.
        """
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            ListStoriesParams
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # Build the query
        query_parts = []
        
        if validated_params.state:
            query_parts.append(f"state={validated_params.state}")
        if validated_params.assignment_group:
            query_parts.append(f"assignment_group={validated_params.assignment_group}")
        
        # Handle timeframe filtering
        if validated_params.timeframe:
            now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
            if validated_params.timeframe == "upcoming":
                query_parts.append(f"start_date>{now}")
            elif validated_params.timeframe == "in-progress":
                query_parts.append(f"start_date<{now}^end_date>{now}")
            elif validated_params.timeframe == "completed":
                query_parts.append(f"end_date<{now}")
        
        # Add any additional query string
        if validated_params.query:
            query_parts.append(validated_params.query)
        
        # Combine query parts
        query = "^".join(query_parts) if query_parts else ""
        
        # Get the instance URL
        instance_url = _get_instance_url(auth_manager, server_config)
        if not instance_url:
            return {
                "success": False,
                "message": "Cannot find instance_url in either server_config or auth_manager",
            }
        
        # Get the headers
        headers = _get_headers(auth_manager, server_config)
        if not headers:
            return {
                "success": False,
                "message": "Cannot find get_headers method in either auth_manager or server_config",
            }
        
        # Make the API request
        url = f"{instance_url}/api/now/table/rm_story"
        
        params = {
            "sysparm_limit": validated_params.limit,
            "sysparm_offset": validated_params.offset,
            "sysparm_query": query,
            "sysparm_display_value": "true",
        }
        
        try:
            response = requests.get(url, headers=headers, params=params)
            response.raise_for_status()
            
            result = response.json()
            
            # Handle the case where result["result"] is a list
            stories = result.get("result", [])
            count = len(stories)
            
            return {
                "success": True,
                "stories": stories,
                "count": count,
                "total": count,  # Use count as total if total is not provided
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error listing stories: {e}")
            return {
                "success": False,
                "message": f"Error listing stories: {str(e)}",
            }
  • Pydantic BaseModel defining the input parameters for the list_stories tool, including limits, filters, and query options.
    class ListStoriesParams(BaseModel):
        """Parameters for listing stories."""
    
        limit: Optional[int] = Field(10, description="Maximum number of records to return")
        offset: Optional[int] = Field(0, description="Offset to start from")
        state: Optional[str] = Field(None, description="Filter by state")
        assignment_group: Optional[str] = Field(None, description="Filter by assignment group")
        timeframe: Optional[str] = Field(None, description="Filter by timeframe (upcoming, in-progress, completed)")
        query: Optional[str] = Field(None, description="Additional query string")
  • Registration of the 'list_stories' tool in the central tool_definitions dictionary, mapping name to implementation, params, description, etc.
    "list_stories": (
        list_stories_tool,
        ListStoriesParams,
        str,  # Expects JSON string
        "List stories from ServiceNow",
        "json",  # Tool returns list/dict
    ),
  • Import of list_stories function into the tools package __init__.py for easy access.
    from servicenow_mcp.tools.story_tools import (
        create_story,
        update_story,
        list_stories,
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 for behavioral disclosure. 'List stories' implies a read operation, but the description doesn't mention pagination behavior (though parameters suggest it), authentication requirements, rate limits, or what format/structure the returned stories will have. This leaves significant gaps for an agent trying to use the tool effectively.

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 states the core purpose without unnecessary words. It's appropriately sized for a list operation and front-loads the essential information.

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 tool with 6 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what a 'story' represents in ServiceNow context, what fields are returned, how results are ordered, or any behavioral constraints. The agent would struggle to use this tool effectively without additional context.

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?

With 100% schema description coverage, the schema already documents all 6 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline expectation but doesn't provide extra value like explaining how parameters interact or providing usage examples.

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 ('List') and resource ('stories from ServiceNow'), making the tool's purpose immediately understandable. However, it doesn't differentiate this tool from other list_* siblings in the server, which would require specifying what makes stories distinct from other entities like incidents or change requests.

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. With many other list_* tools available (list_incidents, list_change_requests, etc.), the agent receives no help in understanding that this tool specifically retrieves stories rather than other ServiceNow record types.

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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