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ServiceNow MCP Server

by javerthl

create_changeset

Create a new changeset in ServiceNow to organize and track application modifications, requiring a name and application identifier for configuration management.

Instructions

Create a new changeset in ServiceNow

Input Schema

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

Implementation Reference

  • Main handler function implementing the create_changeset tool. Validates input parameters using Pydantic model, constructs the ServiceNow API request to create a new changeset in the sys_update_set table, handles authentication and errors, and returns the result.
    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")
  • Tool registration entry in get_tool_definitions() function, mapping 'create_changeset' to its handler, input schema, description, and serialization details for use in the MCP server.
    "create_changeset": (
        create_changeset_tool,
        CreateChangesetParams,
        str,  # Expects JSON string
        "Create a new changeset in ServiceNow",
        "json_dict",  # Tool returns Pydantic model
    ),
  • Export of create_changeset function from tools package, making it available for import in tool_utils.py.
    create_changeset,
  • Helper function used by create_changeset (and other tools) to unwrap dictionary or Pydantic model parameters, validate against the schema, check required fields, and return validated params or error.
    def _unwrap_and_validate_params(
        params: Union[Dict[str, Any], BaseModel], 
        model_class: Type[T], 
        required_fields: Optional[List[str]] = None
    ) -> Dict[str, Any]:
        """
        Unwrap and validate parameters.
    
        Args:
            params: The parameters to unwrap and validate. Can be a dictionary or a Pydantic model.
            model_class: The Pydantic model class to validate against.
            required_fields: List of fields that must be present.
    
        Returns:
            A dictionary with success status and validated parameters or error message.
        """
        try:
            # Handle case where params is already a Pydantic model
            if isinstance(params, BaseModel):
                # If it's already the correct model class, use it directly
                if isinstance(params, model_class):
                    model_instance = params
                # Otherwise, convert to dict and create new instance
                else:
                    model_instance = model_class(**params.dict())
            # Handle dictionary case
            else:
                # Create model instance
                model_instance = model_class(**params)
            
            # Check required fields
            if required_fields:
                missing_fields = []
                for field in required_fields:
                    if getattr(model_instance, field, None) is None:
                        missing_fields.append(field)
                
                if missing_fields:
                    return {
                        "success": False,
                        "message": f"Missing required fields: {', '.join(missing_fields)}",
                    }
            
            return {
                "success": True,
                "params": model_instance,
            }
        except Exception as e:
            return {
                "success": False,
                "message": f"Invalid parameters: {str(e)}",
            }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Create' implying a write operation, but doesn't mention permissions required, whether it's idempotent, what happens on failure, or the expected output format. For a mutation tool with zero annotation coverage, this leaves critical behavioral traits unspecified.

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 front-loads the core action, though its brevity contributes to gaps in other dimensions.

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 this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what a changeset is, how it fits into ServiceNow workflows, what gets returned, or error conditions. For a tool in a complex domain with many siblings, 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 the schema fully documents all 4 parameters (name, application, description, developer) with their types and requirements. The description adds no parameter-specific information beyond what's in the schema, meeting the baseline of 3 when schema does the heavy lifting.

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 'Create a new changeset in ServiceNow' clearly states the verb ('Create') and resource ('changeset'), but it's vague about what a changeset is or its purpose. It doesn't distinguish from siblings like 'create_change_request' or 'create_project', leaving the agent to infer differences from tool names alone.

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 'create_change_request' or 'update_changeset'. The description lacks context about prerequisites, typical workflows, or exclusions, offering no help for tool selection among many creation-related siblings.

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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