get_models

THE RISK

Low Risk

Why get_models needs a policy

This tool retrieves or queries available LLM models, which is a non-destructive information lookup operation. No data is created, modified, deleted, or executed. The blast radius of misuse is minimal—an agent could only learn what models are available, which is informational metadata. The incomplete description ('If you omit the model parameter in \') does not suggest any side effects or capabilities beyond listing.

From the tool's definition Tool name is 'get_models' and description states 'List all available LLM models'. The verb 'list' indicates a read operation that retrieves information without modification or side effects.

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

POLICY

How to control get_models

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

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "get_models": {}
  }
}

get_models is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.

Create a free account and register Cloud Agent MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

CAP THIS TOOL →

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EXPLORE

Related tools and policies

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All 9 Cloud Agent MCP Server tools →

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Go deeper

The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Data exfiltration → How read access becomes an exfiltration channel when chained with untrusted content.
MCP token cost → What connecting this server costs in context-window tokens on every request.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about get_models

What does the get_models tool do? +

List all available LLM models for cloud tasks. If you omit the model parameter in \. It is categorised as a Read tool in the Cloud Agent MCP Server MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on get_models? +

Register the Cloud Agent MCP Server MCP server in PolicyLayer and add a rule for get_models: 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 Cloud Agent MCP Server. Nothing to install.

What risk level is get_models? +

get_models is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit get_models? +

Yes. Add a rate_limit block to the get_models 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 get_models completely? +

Set action: deny in the PolicyLayer policy for get_models. 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 get_models? +

get_models is provided by the Cloud Agent MCP Server MCP server (jxnl/cursor-cloud-agent-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Cloud Agent MCP Server tool call.

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

CHECK YOUR STACK →