feedback_fix
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.
This record as markdown: /tools/io-github-kivanccakmak-yaver/feedback-fix.md
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.
| 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 feedback_fix is rated Medium
The tool creates a task/ticket with feedback data, which is a write operation that modifies system state by adding a new work item. The severity is medium because creating tasks can affect development workflow and resource allocation, but the effect is reversible (tasks can be cancelled/deleted). It does not directly execute fixes (which would be Execute) or destroy data (Destructive).
From the tool's definition 'Create a task' indicates the tool creates a new record/task reversibly. It pulls stack trace and context but does not execute fixes autonomously—it requests an AI runner to perform the fix, making it a task creation (Write) rather than direct code execution…
Attacks that exploit this kind of access
The rule that runs feedback_fix safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Yaver, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For feedback_fix, this is the rule to start with:
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.
The button opens the PolicyLayer dashboard: create your workspace, connect Yaver, apply this rule, and every feedback_fix call is checked against it from then on.
Questions about feedback_fix
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.
feedback_fix accepts 2 parameters: id, runner. Required: id. The full parameter table on this page comes from the server's own tool schema.
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.
feedback_fix is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
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.
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.
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.
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