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vparlapalli490

ServiceNow MCP Server

create_changeset

Create a new changeset in ServiceNow to manage application updates and track development changes with assigned developers and descriptions.

Instructions

Create a new changeset in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName of the changeset
descriptionNoDescription of the changeset
applicationYesApplication the changeset belongs to
developerNoDeveloper responsible for the changeset

Implementation Reference

  • The main handler function that implements the create_changeset tool. It validates parameters using CreateChangesetParams, prepares data, and makes a POST request to the ServiceNow sys_update_set table to create a new changeset.
    def create_changeset(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Union[Dict[str, Any], CreateChangesetParams],
    ) -> Dict[str, Any]:
        """
        Create a new changeset in ServiceNow.
    
        Args:
            auth_manager: The authentication manager.
            server_config: The server configuration.
            params: The parameters for creating a changeset. Can be a dictionary or a CreateChangesetParams object.
    
        Returns:
            The created changeset.
        """
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            CreateChangesetParams, 
            required_fields=["name", "application"]
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # Prepare the request data
        data = {
            "name": validated_params.name,
            "application": validated_params.application,
        }
        
        # Add optional fields if provided
        if validated_params.description:
            data["description"] = validated_params.description
        if validated_params.developer:
            data["developer"] = validated_params.developer
        
        # 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",
            }
        
        # Add Content-Type header
        headers["Content-Type"] = "application/json"
        
        # Make the API request
        url = f"{instance_url}/api/now/table/sys_update_set"
        
        try:
            response = requests.post(url, json=data, headers=headers)
            response.raise_for_status()
            
            result = response.json()
            
            return {
                "success": True,
                "message": "Changeset created successfully",
                "changeset": result["result"],
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error creating changeset: {e}")
            return {
                "success": False,
                "message": f"Error creating changeset: {str(e)}",
            }
  • Pydantic BaseModel defining the input schema for the create_changeset tool, including required fields name and application, and optional description and developer.
    class CreateChangesetParams(BaseModel):
        """Parameters for creating a changeset."""
    
        name: str = Field(..., description="Name of the changeset")
        description: Optional[str] = Field(None, description="Description of the changeset")
        application: str = Field(..., description="Application the changeset belongs to")
        developer: Optional[str] = Field(None, description="Developer responsible for the changeset")
  • Registration of the create_changeset tool in the central tool definitions dictionary used by the MCP server. Maps the tool name to its handler (aliased as create_changeset_tool), input schema, return type, description, and serialization method.
    "create_changeset": (
        create_changeset_tool,
        CreateChangesetParams,
        str,  # Expects JSON string
        "Create a new changeset in ServiceNow",
        "json_dict",  # Tool returns Pydantic model
    ),
  • Import and alias of the create_changeset function as create_changeset_tool for use in tool registration.
        create_changeset as create_changeset_tool,
    )
  • Export of create_changeset in the tools __init__.py __all__ list, making it available for imports.
    "create_changeset",
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. While 'Create' implies a write operation, the description doesn't address permissions needed, whether the changeset becomes active immediately, what happens on success/failure, or any side effects. For a mutation tool with zero annotation coverage, this is inadequate.

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 creation tool and immediately communicates the core purpose without unnecessary elaboration.

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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what a changeset is in ServiceNow context, what happens after creation, or what the tool returns. Given the complexity implied by sibling tools and lack of structured metadata, 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 itself. The description adds no additional parameter information beyond what's already in the structured schema, meeting the baseline expectation but not providing extra value.

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 ('Create') and resource ('a new changeset in ServiceNow'), making the purpose immediately understandable. However, it doesn't differentiate this tool from similar siblings like 'create_change_request' or 'create_article' beyond the resource name, which prevents a perfect score.

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 like 'create_change_request' or 'update_changeset', nor does it mention prerequisites or context for creating changesets. With many sibling tools in the ServiceNow domain, this lack of differentiation is a significant gap.

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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