wait_for_app_ready · Manifest MCP: Risk & Policy

WHAT IT DOES

What wait_for_app_ready does on Manifest MCP

AI agents invoke wait_for_app_ready to trigger actions in Manifest MCP. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.

WHY IT NEEDS A POLICY

Why wait_for_app_ready needs a policy

This tool executes a blocking operation that interacts with external provider infrastructure to monitor and validate deployment state. While it does not directly create, modify, or delete resources, it performs an asynchronous polling loop with conditional exception handling on an external system, which is characteristic of Execute-category tools.

From the tool's definition The tool performs a polling operation that waits for an app deployment to reach ACTIVE state, actively monitoring and throwing exceptions on timeout or terminal lease states.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

wait_for_app_ready Rate-limited

wait_for_app_ready stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "wait_for_app_ready": {
      "limits": [
        {
          "counter": "wait_for_app_ready_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Manifest MCP, so you can run agents against it safely.

FAQ

Questions about wait_for_app_ready

What does the wait_for_app_ready tool do? +

Wait for a deployed app to reach the ACTIVE state on the provider, polling at the configured interval. Use this after deploy_app instead of looping app_status manually. Throws on timeout or terminal lease state. It is categorised as a Execute tool in the Manifest MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on wait_for_app_ready? +

Register the Manifest MCP server in PolicyLayer and add a rule for wait_for_app_ready: 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 Manifest MCP. Nothing to install.

What risk level is wait_for_app_ready? +

wait_for_app_ready is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit wait_for_app_ready? +

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

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

wait_for_app_ready is provided by the Manifest MCP server (manifest-network/manifest-mcp-mono). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.