Medium Risk

enhance_prompt

Uses an LLM (via OpenRouter) to refine a prompt for video generation.

How to control enhance_prompt ↓

What enhance_prompt does on RunwayML + Luma AI MCP Server

AI agents use enhance_prompt to create or update resources in RunwayML + Luma AI MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your RunwayML + Luma AI MCP Server environment.

Medium Risk

Why enhance_prompt needs a policy

This tool falls into the Write category because it creates or modifies data (the enhanced prompt) in a reversible manner. While it calls an LLM via OpenRouter, the actual output is a text modification, not execution of arbitrary code or external operations.

From the tool's definition The tool 'enhance_prompt' uses an LLM to refine a prompt, which results in creating or modifying prompt text that is then used for subsequent video generation.

Documented attack patterns abuse exactly the kind of access enhance_prompt gives an agent:

How to control enhance_prompt

PolicyLayer is an MCP gateway — it sits between your AI agents and RunwayML + Luma AI MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for enhance_prompt:

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "enhance_prompt": {
      "limits": [
        {
          "counter": "enhance_prompt_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

enhance_prompt 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.

  1. Create a free account and register RunwayML + Luma AI MCP Server — nothing to install.
  2. Add this policy — paste it, or build it visually.
  3. Point your MCP client (Claude, Cursor, anything) at your gateway URL.
LIMIT THIS TOOL →

Free to start. No card required.

Related tools and policies

Go deeper

Questions about enhance_prompt

What does the enhance_prompt tool do? +

Uses an LLM (via OpenRouter) to refine a prompt for video generation. It is categorised as a Write tool in the RunwayML + Luma AI MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on enhance_prompt? +

Register the RunwayML + Luma AI MCP Server MCP server in PolicyLayer and add a rule for enhance_prompt: 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 RunwayML + Luma AI MCP Server. Nothing to install.

What risk level is enhance_prompt? +

enhance_prompt is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit enhance_prompt? +

Yes. Add a rate_limit block to the enhance_prompt 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.

How do I block enhance_prompt completely? +

Set action: deny in the PolicyLayer policy for enhance_prompt. 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.

What MCP server provides enhance_prompt? +

enhance_prompt is provided by the RunwayML + Luma AI MCP Server MCP server (wheattoast11/mcp-video-gen). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every RunwayML + Luma AI MCP Server tool call.

Start from RunwayML + Luma AI MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

Free to start. No card required.

10 RunwayML + Luma AI MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.

// GET IN TOUCH

Have a question or want to learn more? Send us a message.

Message sent.

We'll get back to you soon.