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javerthl

ServiceNow MCP Server

by javerthl

list_stories

Retrieve and filter stories from ServiceNow with options to limit results, apply state filters, and search by assignment group or timeframe.

Instructions

List stories from ServiceNow

Input Schema

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

Implementation Reference

  • The handler function that implements the list_stories tool logic: validates params, builds ServiceNow query, makes GET request to rm_story table, returns stories list.
    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 model defining input parameters for the list_stories tool.
    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 (handler, params schema, return type, description, serialization).
    "list_stories": (
        list_stories_tool,
        ListStoriesParams,
        str,  # Expects JSON string
        "List stories from ServiceNow",
        "json",  # Tool returns list/dict
    ),
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. 'List stories' implies a read-only operation, but the description doesn't disclose pagination behavior (implied by limit/offset parameters), authentication requirements, rate limits, or what format/structure the returned stories will have. For a listing tool with 6 parameters, 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 with zero wasted words. It's appropriately sized for a listing operation and front-loads the essential information (verb + resource).

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 tool has 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what constitutes a 'story' in ServiceNow context, what fields are returned, or how results are structured. For a listing tool in a complex system with many similar entities, more context is needed.

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 all parameters are documented in the schema. The description adds no additional parameter information beyond what's already in the structured schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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 ('List') and resource ('stories from ServiceNow'), making the purpose immediately understandable. It doesn't explicitly distinguish from sibling tools like 'list_epics' or 'list_scrum_tasks', but the resource specificity is adequate for basic understanding.

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 sibling list tools (list_epics, list_scrum_tasks, list_story_dependencies, etc.), there's no indication of what distinguishes 'stories' from these other entities or when an agent should choose this specific listing tool.

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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