save_results_for_mirror · Langfuse Observability MCP

WHAT IT DOES

What save_results_for_mirror does on Langfuse Observability

AI agents use save_results_for_mirror to create or update resources in Langfuse Observability — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Langfuse Observability environment.

WHY IT NEEDS A POLICY

Why save_results_for_mirror needs a policy

This tool modifies exam result data, which is a Write operation. Severity is high because misuse could affect academic integrity by saving incorrect exam results for students, potentially leading to inappropriate credentials or grade records, though the action itself is not destructive (results can be corrected).

From the tool's definition Tool name 'save_results_for_mirror' and description 'Save proctor exam results for an unofficial mirror' indicate the tool creates or modifies exam result records.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

save_results_for_mirror Rate-limited

save_results_for_mirror 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.

View as policy code

policy.json

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

LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Langfuse Observability, so you can run agents against it safely.

FAQ

Questions about save_results_for_mirror

What does the save_results_for_mirror tool do? +

Save proctor exam results for an unofficial mirror. Pass the \. It is categorised as a Write tool in the Langfuse Observability MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on save_results_for_mirror? +

Register the Langfuse Observability MCP server in PolicyLayer and add a rule for save_results_for_mirror: 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 Langfuse Observability. Nothing to install.

What risk level is save_results_for_mirror? +

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

Can I rate-limit save_results_for_mirror? +

Yes. Add a rate_limit block to the save_results_for_mirror 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 save_results_for_mirror completely? +

Set action: deny in the PolicyLayer policy for save_results_for_mirror. 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 save_results_for_mirror? +

save_results_for_mirror is provided by the Langfuse Observability MCP server (langfuse-observability-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.