Submit an asynchronous job to a Serverless endpoint. Returns a job ID immediately — use get-job-status to poll for results. Async results are available for 30 minutes after completion.
AI agents invoke run-endpoint to trigger actions in RunPod MCP Server. 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.
This tool executes code/jobs on remote RunPod infrastructure, making it an Execute category tool. Severity is high because misuse could trigger arbitrary workloads consuming significant computational resources and incurring costs. The blast radius includes unintended job execution, resource exhaustion, and potential financial impact.
From the tool's definition The tool description states it 'Submit[s] an asynchronous job to a Serverless endpoint' and 'Returns a job ID immediately', indicating it triggers external operations (running jobs on remote serverless infrastructure).
Documented attack patterns abuse exactly the kind of access run-endpoint gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and RunPod MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for run-endpoint:
{
"version": "1",
"default": "deny",
"tools": {
"run-endpoint": {
"limits": [
{
"counter": "run-endpoint_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}run-endpoint 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.
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Submit an asynchronous job to a Serverless endpoint. Returns a job ID immediately — use get-job-status to poll for results. Async results are available for 30 minutes after completion. It is categorised as a Execute tool in the RunPod MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the RunPod MCP Server MCP server in PolicyLayer and add a rule for run-endpoint: 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 RunPod MCP Server. Nothing to install.
run-endpoint is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the run-endpoint 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 run-endpoint. 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.
run-endpoint is provided by the RunPod MCP Server MCP server (runpod/runpod-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 36 RunPod MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
36 RunPod MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.