Apply virtual clothing try-on to a person image using AI. Upload a person image and up to 5 clothing items to see how they would look wearing those clothes. Supports both single and multiple clothing combinations for complete outfit visualization.
AI agents use virtual_try_on to create or update resources in MCP Kling — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your MCP Kling environment.
An AI agent can call virtual_try_on faster than any human can review — one bad instruction and it creates or modifies resources in MCP Kling by the hundred, each call as confident as the last.
Documented attack patterns abuse exactly the kind of access virtual_try_on gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and MCP Kling, and nothing reaches the server without passing your rules. This is the rule we recommend for virtual_try_on:
{
"version": "1",
"default": "deny",
"tools": {
"virtual_try_on": {
"limits": [
{
"counter": "virtual_try_on_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}virtual_try_on stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Apply virtual clothing try-on to a person image using AI. Upload a person image and up to 5 clothing items to see how they would look wearing those clothes. Supports both single and multiple clothing combinations for complete outfit visualization. It is categorised as a Write tool in the MCP Kling MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the MCP Kling MCP server in PolicyLayer and add a rule for virtual_try_on: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches MCP Kling. Nothing to install.
virtual_try_on is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the virtual_try_on rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for virtual_try_on. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
virtual_try_on is provided by the MCP Kling MCP server (199-mcp/mcp-kling). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 12 MCP Kling tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
12 MCP Kling tools catalogued and risk-classified — across an index of 42,500+ MCP servers.