kage_feedback

THE RISK

Medium Risk

Why kage_feedback needs a policy

This tool creates or updates feedback metadata on memory packets without irreversible deletion, code execution, financial impact, or destructive side effects. It is a straightforward Write operation that records user opinions/assessments.

From the tool's definition Tool description states 'Record usefulness feedback' which indicates creating or modifying feedback data. The tool operates on an 'approved repo-local memory packet' with feedback values (helpful, wrong, or stale) that are reversible annotations.

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

POLICY

How to control kage_feedback

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

policy.json

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

kage_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.

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

LIMIT THIS TOOL →

Free to start. No card required.

EXPLORE

Related tools and policies

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Go deeper

The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Argument validation → Checking tool call arguments against schema and policy before they run.
Scoped token → Per-person credentials that grant a defined subset of servers and tools.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about kage_feedback

What does the kage_feedback tool do? +

Record usefulness feedback on an approved repo-local memory packet: helpful, wrong, or stale. It is categorised as a Write tool in the Kage MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on kage_feedback? +

Register the Kage MCP server in PolicyLayer and add a rule for kage_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 Kage. Nothing to install.

What risk level is kage_feedback? +

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

Can I rate-limit kage_feedback? +

Yes. Add a rate_limit block to the kage_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 kage_feedback completely? +

Set action: deny in the PolicyLayer policy for kage_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 kage_feedback? +

kage_feedback is provided by the Kage MCP server (@kage-core/kage-graph-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Kage tool call.

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

CHECK YOUR STACK →