Medium Risk

submit_feedback

Sends the user's product feedback about agentView to an internal review queue. Use this ONLY when the user explicitly wants to share feedback, a feature request, a complaint, or praise about agentView itself (not about the content shown on a display). Always confirm the wording with the user befo...

Risk signalsHandles credentials or secrets (access_token)

Part of the agentView server.

submit_feedback can modify agentView data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use submit_feedback to create or modify resources in agentView. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call submit_feedback repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach agentView.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "submit_feedback": {
      "limits": [
        {
          "counter": "submit_feedback_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full agentView policy for all 81 tools.

Get this rule live on your own agentView server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access submit_feedback gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so submit_feedback only ever does what you allow.

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Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the submit_feedback tool do? +

Sends the user's product feedback about agentView to an internal review queue. Use this ONLY when the user explicitly wants to share feedback, a feature request, a complaint, or praise about agentView itself (not about the content shown on a display). Always confirm the wording with the user before sending; never invent or embellish feedback on their behalf. Requires authentication with at least content_only scope. The feedback is stored for later review; no automatic reply is sent and this does not open a support ticket. Returns the new feedback id and a status of 'received'.. It is categorised as a Write tool in the agentView MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on submit_feedback? +

Register the agentView MCP server in PolicyLayer and add a rule for submit_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 agentView. Nothing to install.

What risk level is submit_feedback? +

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

Can I rate-limit submit_feedback? +

Yes. Add a rate_limit block to the submit_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 submit_feedback completely? +

Set action: deny in the PolicyLayer policy for submit_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 submit_feedback? +

submit_feedback is provided by the agentView MCP server (https://agentview.de/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every agentView tool call.

Deterministic rules across all 81 agentView tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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