retry_good_job · Langfuse Observability MCP: Risk & Policy

WHAT IT DOES

What retry_good_job does on Langfuse Observability

AI agents invoke retry_good_job to trigger actions in Langfuse Observability. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.

WHY IT NEEDS A POLICY

Why retry_good_job needs a policy

This tool re-enqueues and executes a background job, triggering external operations. It is not merely reading or writing data — it causes a job to run again, which could have side effects depending on the job's nature. The severity is medium because misuse could cause duplicate processing or unintended re-execution of jobs, but it's limited to already-existing failed jobs.

From the tool's definition Retry a failed or discarded GoodJob background job. Re-enqueues the job for processing.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

retry_good_job Rate-limited

retry_good_job stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "retry_good_job": {
      "limits": [
        {
          "counter": "retry_good_job_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-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 retry_good_job

What does the retry_good_job tool do? +

Retry a failed or discarded GoodJob background job. Re-enqueues the job for processing. Use cases: - Retry a job that failed due to a transient error - Re-process a discarded job after fixing the underlying issue. It is categorised as a Execute tool in the Langfuse Observability MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on retry_good_job? +

Register the Langfuse Observability MCP server in PolicyLayer and add a rule for retry_good_job: 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 retry_good_job? +

retry_good_job is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit retry_good_job? +

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

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

retry_good_job 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.