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vparlapalli490

ServiceNow MCP Server

update_changeset

Modify an existing ServiceNow changeset by updating its name, description, state, or assigned developer to track and manage IT change processes.

Instructions

Update an existing changeset in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
changeset_idYesChangeset ID or sys_id
nameNoName of the changeset
descriptionNoDescription of the changeset
stateNoState of the changeset
developerNoDeveloper responsible for the changeset

Implementation Reference

  • The core handler function that implements the logic for updating a changeset in ServiceNow by making a PATCH request to the sys_update_set table.
    def update_changeset(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Union[Dict[str, Any], UpdateChangesetParams],
    ) -> Dict[str, Any]:
        """
        Update an existing changeset in ServiceNow.
    
        Args:
            auth_manager: The authentication manager.
            server_config: The server configuration.
            params: The parameters for updating a changeset. Can be a dictionary or a UpdateChangesetParams object.
    
        Returns:
            The updated changeset.
        """
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            UpdateChangesetParams, 
            required_fields=["changeset_id"]
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # Prepare the request data
        data = {}
        
        # Add optional fields if provided
        if validated_params.name:
            data["name"] = validated_params.name
        if validated_params.description:
            data["description"] = validated_params.description
        if validated_params.state:
            data["state"] = validated_params.state
        if validated_params.developer:
            data["developer"] = validated_params.developer
        
        # If no fields to update, return error
        if not data:
            return {
                "success": False,
                "message": "No fields to update",
            }
        
        # 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/{validated_params.changeset_id}"
        
        try:
            response = requests.patch(url, json=data, headers=headers)
            response.raise_for_status()
            
            result = response.json()
            
            return {
                "success": True,
                "message": "Changeset updated successfully",
                "changeset": result["result"],
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error updating changeset: {e}")
            return {
                "success": False,
                "message": f"Error updating changeset: {str(e)}",
            }
  • Pydantic BaseModel defining the input schema and validation for the update_changeset tool parameters.
    class UpdateChangesetParams(BaseModel):
        """Parameters for updating a changeset."""
    
        changeset_id: str = Field(..., description="Changeset ID or sys_id")
        name: Optional[str] = Field(None, description="Name of the changeset")
        description: Optional[str] = Field(None, description="Description of the changeset")
        state: Optional[str] = Field(None, description="State of the changeset")
        developer: Optional[str] = Field(None, description="Developer responsible for the changeset")
  • Tool registration entry in get_tool_definitions() dictionary, mapping the tool name to its handler, input schema, return type, description, and serialization method.
    "update_changeset": (
        update_changeset_tool,
        UpdateChangesetParams,
        str,  # Expects JSON string
        "Update an existing changeset in ServiceNow",
        "json_dict",  # Tool returns Pydantic model
    ),
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure but only states the basic action. It doesn't mention required permissions, whether updates are reversible, what happens to unspecified fields, error conditions, or typical response format. For a mutation tool with zero annotation coverage, this leaves significant 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 that gets straight to the point with zero wasted words. It's appropriately sized for a tool with good schema documentation and is perfectly front-loaded.

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 'update' entails operationally, what values can be modified, what the typical response looks like, or any behavioral constraints. The combination of mutation operation + zero annotation coverage requires more descriptive context.

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 fields. Baseline score of 3 is appropriate when the schema does the heavy lifting.

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 ('Update') and resource ('an existing changeset in ServiceNow'), making the purpose immediately understandable. It distinguishes from sibling tools like 'create_changeset' by specifying 'existing', but doesn't explicitly differentiate from other update tools (e.g., 'update_change_request').

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 about when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing a changeset ID), when to choose this over 'create_changeset' or 'update_change_request', or any context about typical update scenarios.

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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