Discard a GoodJob background job. Marks the job as discarded so it will not be retried. Use cases: - Discard a job that is no longer needed - Stop a failing job from being retried - Clean up jobs that are stuck or obsolete
AI agents call discard_good_job to permanently remove resources in Langfuse Observability — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
This tool irreversibly alters job state by marking jobs as discarded, preventing retry attempts. While not a data deletion in the traditional sense, it permanently removes jobs from active processing and cannot be undone without manual intervention or job re-creation. The use case 'Stop a failing job from being retried' and 'Clean up jobs that are stuck or obsolete' confirm the irreversible nature.
From the tool's definition 'Discard a GoodJob background job. Marks the job as discarded so it will not be retried.' — the tool permanently marks jobs as discarded, preventing future retries and effectively removing them from the job queue. This is an irreversible state change.
Attacks that exploit this kind of access
Discard a GoodJob background job. Marks the job as discarded so it will not be retried. Use cases: - Discard a job that is no longer needed - Stop a failing job from being retried - Clean up jobs that are stuck or obsolete. It is categorised as a Destructive tool in the Langfuse Observability MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Langfuse Observability MCP server in PolicyLayer and add a rule for discard_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.
discard_good_job is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the discard_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.
Set action: deny in the PolicyLayer policy for discard_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.
discard_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.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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