Skip to main content
Glama
echelon-ai-labs

ServiceNow MCP Server

get_script_include

Retrieve a specific script include by ID or name from ServiceNow using the ServiceNow MCP Server to streamline development and debugging processes.

Instructions

Get a specific script include from ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Implementation Reference

  • The handler function that executes the get_script_include tool, querying the ServiceNow API for script include details by ID or name.
    def get_script_include(
        config: ServerConfig,
        auth_manager: AuthManager,
        params: GetScriptIncludeParams,
    ) -> Dict[str, Any]:
        """Get a specific script include from ServiceNow.
        
        Args:
            config: The server configuration.
            auth_manager: The authentication manager.
            params: The parameters for the request.
            
        Returns:
            A dictionary containing the script include data.
        """
        try:
            # Build query parameters
            query_params = {
                "sysparm_display_value": "true",
                "sysparm_exclude_reference_link": "true",
                "sysparm_fields": "sys_id,name,script,description,api_name,client_callable,active,access,sys_created_on,sys_updated_on,sys_created_by,sys_updated_by"
            }
            
            # Determine if we're querying by sys_id or name
            if params.script_include_id.startswith("sys_id:"):
                sys_id = params.script_include_id.replace("sys_id:", "")
                url = f"{config.instance_url}/api/now/table/sys_script_include/{sys_id}"
            else:
                # Query by name
                url = f"{config.instance_url}/api/now/table/sys_script_include"
                query_params["sysparm_query"] = f"name={params.script_include_id}"
                
            # Make the request
            headers = auth_manager.get_headers()
            
            response = requests.get(
                url,
                params=query_params,
                headers=headers,
                timeout=30,
            )
            response.raise_for_status()
            
            # Parse the response
            data = response.json()
            
            if "result" not in data:
                return {
                    "success": False,
                    "message": f"Script include not found: {params.script_include_id}",
                }
                
            # Handle both single result and list of results
            result = data["result"]
            if isinstance(result, list):
                if not result:
                    return {
                        "success": False,
                        "message": f"Script include not found: {params.script_include_id}",
                    }
                item = result[0]
            else:
                item = result
                
            script_include = {
                "sys_id": item.get("sys_id"),
                "name": item.get("name"),
                "script": item.get("script"),
                "description": item.get("description"),
                "api_name": item.get("api_name"),
                "client_callable": item.get("client_callable") == "true",
                "active": item.get("active") == "true",
                "access": item.get("access"),
                "created_on": item.get("sys_created_on"),
                "updated_on": item.get("sys_updated_on"),
                "created_by": item.get("sys_created_by", {}).get("display_value"),
                "updated_by": item.get("sys_updated_by", {}).get("display_value"),
            }
            
            return {
                "success": True,
                "message": f"Found script include: {item.get('name')}",
                "script_include": script_include,
            }
            
        except Exception as e:
            logger.error(f"Error getting script include: {e}")
            return {
                "success": False,
                "message": f"Error getting script include: {str(e)}",
            }
  • Pydantic model for input validation of the get_script_include tool parameters.
    class GetScriptIncludeParams(BaseModel):
        """Parameters for getting a script include."""
        
        script_include_id: str = Field(..., description="Script include ID or name")
  • Tool registration entry in the central tool definitions dictionary used by the MCP server.
    "get_script_include": (
        get_script_include_tool,
        GetScriptIncludeParams,
        Dict[str, Any],  # Expects dict
        "Get a specific script include from ServiceNow",
        "raw_dict",  # Tool returns raw dict
    ),
  • Import and alias of the handler function for use in tool registration.
    from servicenow_mcp.tools.script_include_tools import (
        get_script_include as get_script_include_tool,
    )
  • Re-export of script include tools including get_script_include for convenient access.
    from servicenow_mcp.tools.script_include_tools import (
        create_script_include,
        delete_script_include,
        get_script_include,
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. It states 'Get' which implies a read operation, but doesn't disclose behavioral traits like authentication needs, rate limits, error handling, or what happens if the script include doesn't exist. For a tool with zero annotation coverage, this is a significant gap.

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 waste, front-loading the core purpose. It's appropriately sized for a simple retrieval tool.

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 no annotations, no output schema, and low schema description coverage (0%), the description is incomplete. It lacks details on behavior, parameters, return values, and usage context, making it inadequate for a tool that retrieves data from a complex system like ServiceNow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, with one parameter ('script_include_id') documented only as 'Script include ID or name' in the schema. The description adds no information about parameter semantics, format, examples, or constraints, failing to compensate for the low coverage.

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 ('Get') and resource ('a specific script include from ServiceNow'), making the purpose understandable. It distinguishes from sibling 'list_script_includes' by specifying 'specific' rather than listing, though it doesn't explicitly name the sibling alternative.

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 'list_script_includes' or 'create_script_include'. The description implies usage for retrieving a single script include but offers no context about prerequisites, error conditions, or comparisons to other tools.

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