turingmind_submit_feedback · TuringMind MCP Server MCP

WHAT IT DOES

What turingmind_submit_feedback does on TuringMind MCP Server

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

WHY IT NEEDS A POLICY

Why turingmind_submit_feedback needs a policy

Submitting feedback is a write operation that creates new data (feedback records) or modifies existing issue states reversibly. While the description is incomplete ('Use this when user indicates' is cut off), the core action is clearly data creation/modification rather than deletion, code execution, financial impact, or read-only retrieval.

From the tool's definition Tool name 'turingmind_submit_feedback' and description 'Submit feedback on a code review issue' indicate the tool creates or modifies feedback data associated with code review issues.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

turingmind_submit_feedback Rate-limited

turingmind_submit_feedback 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "turingmind_submit_feedback": {
      "limits": [
        {
          "counter": "turingmind_submit_feedback_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL →

Instant setup, no code required.

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

FAQ

Questions about turingmind_submit_feedback

What does the turingmind_submit_feedback tool do? +

Submit feedback on a code review issue. Use this when user indicates. It is categorised as a Write tool in the TuringMind 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 turingmind_submit_feedback? +

Register the TuringMind MCP Server MCP server in PolicyLayer and add a rule for turingmind_submit_feedback: 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 TuringMind MCP Server. Nothing to install.

What risk level is turingmind_submit_feedback? +

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

Can I rate-limit turingmind_submit_feedback? +

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

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

turingmind_submit_feedback is provided by the TuringMind MCP Server MCP server (turingmindai/turingmind-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.