Run a post-mortem analysis on negative feedback. Returns a proposed rule and recurrence info.
Part of the MCP Memory Gateway server.
Free to start. No card required.
AI agents invoke reflect_on_feedback to trigger processes or run actions in MCP Memory Gateway. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
reflect_on_feedback can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"reflect_on_feedback": {
"limits": [
{
"counter": "reflect_on_feedback_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full MCP Memory Gateway policy for all 57 tools.
These attack patterns abuse exactly the kind of access reflect_on_feedback gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Run a post-mortem analysis on negative feedback. Returns a proposed rule and recurrence info.. It is categorised as a Execute tool in the MCP Memory Gateway MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the MCP Memory Gateway MCP server in PolicyLayer and add a rule for reflect_on_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 MCP Memory Gateway. Nothing to install.
reflect_on_feedback is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the reflect_on_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.
Set action: deny in the PolicyLayer policy for reflect_on_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.
reflect_on_feedback is provided by the MCP Memory Gateway MCP server (IgorGanapolsky/mcp-memory-gateway). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 57 MCP Memory Gateway tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.