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JLKmach

ServiceNow MCP Server

by JLKmach

list_incidents

Retrieve and filter ServiceNow incident records with pagination, state, assignment, category, and search options for incident management.

Instructions

List incidents from ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of incidents to return
offsetNoOffset for pagination
stateNoFilter by incident state
assigned_toNoFilter by assigned user
categoryNoFilter by category
queryNoSearch query for incidents

Implementation Reference

  • The handler function that executes the list_incidents tool, querying ServiceNow API with parameters for limit, offset, filters, and returning formatted incident list.
    def list_incidents(
        config: ServerConfig,
        auth_manager: AuthManager,
        params: ListIncidentsParams,
    ) -> dict:
        """
        List incidents from ServiceNow.
    
        Args:
            config: Server configuration.
            auth_manager: Authentication manager.
            params: Parameters for listing incidents.
    
        Returns:
            Dictionary with list of incidents.
        """
        api_url = f"{config.api_url}/table/incident"
    
        # Build query parameters
        query_params = {
            "sysparm_limit": params.limit,
            "sysparm_offset": params.offset,
            "sysparm_display_value": "true",
            "sysparm_exclude_reference_link": "true",
        }
        
        # Add filters
        filters = []
        if params.state:
            filters.append(f"state={params.state}")
        if params.assigned_to:
            filters.append(f"assigned_to={params.assigned_to}")
        if params.category:
            filters.append(f"category={params.category}")
        if params.query:
            filters.append(f"short_descriptionLIKE{params.query}^ORdescriptionLIKE{params.query}")
        
        if filters:
            query_params["sysparm_query"] = "^".join(filters)
        
        # Make request
        try:
            response = requests.get(
                api_url,
                params=query_params,
                headers=auth_manager.get_headers(),
                timeout=config.timeout,
            )
            response.raise_for_status()
            
            data = response.json()
            incidents = []
            
            for incident_data in data.get("result", []):
                # Handle assigned_to field which could be a string or a dictionary
                assigned_to = incident_data.get("assigned_to")
                if isinstance(assigned_to, dict):
                    assigned_to = assigned_to.get("display_value")
                
                incident = {
                    "sys_id": incident_data.get("sys_id"),
                    "number": incident_data.get("number"),
                    "short_description": incident_data.get("short_description"),
                    "description": incident_data.get("description"),
                    "state": incident_data.get("state"),
                    "priority": incident_data.get("priority"),
                    "assigned_to": assigned_to,
                    "category": incident_data.get("category"),
                    "subcategory": incident_data.get("subcategory"),
                    "created_on": incident_data.get("sys_created_on"),
                    "updated_on": incident_data.get("sys_updated_on"),
                }
                incidents.append(incident)
            
            return {
                "success": True,
                "message": f"Found {len(incidents)} incidents",
                "incidents": incidents
            }
            
        except requests.RequestException as e:
            logger.error(f"Failed to list incidents: {e}")
            return {
                "success": False,
                "message": f"Failed to list incidents: {str(e)}",
                "incidents": []
            }
  • Pydantic BaseModel defining the input schema/parameters for the list_incidents tool.
    class ListIncidentsParams(BaseModel):
        """Parameters for listing incidents."""
        
        limit: int = Field(10, description="Maximum number of incidents to return")
        offset: int = Field(0, description="Offset for pagination")
        state: Optional[str] = Field(None, description="Filter by incident state")
        assigned_to: Optional[str] = Field(None, description="Filter by assigned user")
        category: Optional[str] = Field(None, description="Filter by category")
        query: Optional[str] = Field(None, description="Search query for incidents")
  • Registers the list_incidents tool in the central tool_definitions dictionary, specifying handler, schema, description, and serialization.
    "list_incidents": (
        list_incidents_tool,
        ListIncidentsParams,
        str,  # Expects JSON string
        "List incidents from ServiceNow",
        "json",  # Tool returns list/dict, needs JSON dump
    ),
  • Imports the list_incidents function for re-export in the tools package.
    from servicenow_mcp.tools.incident_tools import (
        add_comment,
        create_incident,
        list_incidents,
        resolve_incident,
        update_incident,
    )
  • Includes list_incidents in the __all__ list for package export.
    "list_incidents",
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. It doesn't disclose behavioral traits like whether this is a read-only operation, how results are ordered, if there are rate limits, authentication requirements, or what the output format looks like. This is inadequate for a tool with 6 parameters and no output schema.

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 simple 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 the tool returns, how results are structured, or provide any behavioral context beyond the basic action. The agent would need to guess about output format and operational characteristics.

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 the schema already documents all parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, which meets the baseline expectation when schema coverage is complete.

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 incidents from ServiceNow' states the basic verb (list) and resource (incidents) but lacks specificity about scope or differentiation from siblings like 'list_change_requests' or 'list_articles'. It's clear but minimal, falling short of distinguishing this tool from other list tools in the server.

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. The description doesn't mention any context, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone among many similar list tools.

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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