update_annotation_queue · Langfuse Mcp Python MCP

WHAT IT DOES

What update_annotation_queue does on Langfuse Mcp Python

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

WHY IT NEEDS A POLICY

Why update_annotation_queue needs a policy

This tool modifies annotation queue properties but does not delete data or execute arbitrary operations. Updates are typically reversible by issuing subsequent updates with different values. The moderate severity reflects that misconfigured annotation queues could disrupt monitoring workflows or cause data loss if queue rules are altered maliciously, but the change itself is not destructive or irreversible.

From the tool's definition Tool name 'update_annotation_queue' and description 'Update annotation queue properties' indicate modification of existing data structures. The verb 'update' confirms reversible alteration of annotation queue configuration.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

update_annotation_queue Rate-limited

update_annotation_queue 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": {
    "update_annotation_queue": {
      "limits": [
        {
          "counter": "update_annotation_queue_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 Mcp Python, so you can run agents against it safely.

FAQ

Questions about update_annotation_queue

What does the update_annotation_queue tool do? +

Update annotation queue properties. It is categorised as a Write tool in the Langfuse Mcp Python MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on update_annotation_queue? +

Register the Langfuse Mcp Python MCP server in PolicyLayer and add a rule for update_annotation_queue: 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 Mcp Python. Nothing to install.

What risk level is update_annotation_queue? +

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

Can I rate-limit update_annotation_queue? +

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

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

update_annotation_queue is provided by the Langfuse Mcp Python MCP server (log-logn/langfuse-mcp-python). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.