Submit feedback to improve ue-mcp when native tools fall short and execute_python was used as a workaround. Actions: - submit: Submit feedback about a tool gap. Blocks on an MCP elicitation prompt that asks the USER (not the agent) to approve or decline the exact payload before anything is posted...
Part of the Ue server.
Free to start. No card required.
AI agents call feedback to perform operations in Ue. While the risk category is not fully classified, applying a rate limit gives you visibility into how often the tool is called and prevents unexpected bursts of activity from autonomous agents.
Applying a policy to feedback gives you an audit trail of every call an AI agent makes. Even for low-risk tools, visibility into agent behaviour helps you debug issues, optimise workflows, and maintain compliance with your organisation's security requirements.
Apply a rate limit to control usage and monitor for unexpected behaviour.
{
"version": "1",
"default": "deny",
"tools": {
"feedback": {
"limits": [
{
"counter": "feedback_rate",
"window": "minute",
"max": 60,
"scope": "grant"
}
]
}
}
}See the full Ue policy for all 22 tools.
These attack patterns abuse exactly the kind of access feedback gives an agent. Each links to the full case and the policy that stops it:
Other other tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Submit feedback to improve ue-mcp when native tools fall short and execute_python was used as a workaround. Actions: - submit: Submit feedback about a tool gap. Blocks on an MCP elicitation prompt that asks the USER (not the agent) to approve or decline the exact payload before anything is posted to GitHub.. It is categorised as a Other tool in the Ue MCP Server, which means it performs auxiliary operations.
Register the Ue MCP server in PolicyLayer and add a rule for 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 Ue. Nothing to install.
feedback is a Other tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the 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 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.
feedback is provided by the Ue MCP server (ue-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 22 Ue 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.