feedback_fix · Yaver MCP: Risk & Policy

WHAT IT DOES

What feedback_fix does on Yaver

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

PARAMETERS

Parameter	Type	Required	Description
`id`	string	Yes
`runner`	string	—	Optional runner override (claude-code / codex / aider / ...).

Parameters from the server's own tool schema.

WHY IT NEEDS A POLICY

Why feedback_fix needs a policy

An AI agent can call feedback_fix faster than any human can review — one bad instruction and it creates or modifies resources in Yaver by the hundred, each call as confident as the last.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

feedback_fix Rate-limited

feedback_fix 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": {
    "feedback_fix": {
      "limits": [
        {
          "counter": "feedback_fix_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 Yaver, so you can run agents against it safely.

FAQ

Questions about feedback_fix

What does the feedback_fix tool do? +

Create a task that asks the wrapped AI runner to fix the issue captured in this feedback report. Pulls in the stack trace + BlackBox context automatically. It is categorised as a Write tool in the Yaver MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

What parameters does feedback_fix accept? +

feedback_fix accepts 2 parameters: id, runner. Required: id. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on feedback_fix? +

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

What risk level is feedback_fix? +

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

Can I rate-limit feedback_fix? +

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

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

feedback_fix is provided by the Yaver MCP server (yaver-cli). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.