Submit a synchronous job to a Serverless endpoint and wait for the result. Best for tasks completing within 90 seconds. If processing exceeds 90 seconds, the response returns a job ID to poll with get-job-status. Max payload: 20 MB. Results expire after 1 minute. Use the wait parameter to extend ...
AI agents invoke runsync-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 submits and executes jobs on external serverless infrastructure. The effects depend entirely on the endpoint and payload arguments, making it an Execute-category tool.
From the tool's definition "Submit a synchronous job to a Serverless endpoint and wait for the result" — triggers external computation/operations on a RunPod serverless endpoint with arbitrary payloads up to 20 MB
Documented attack patterns abuse exactly the kind of access runsync-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 runsync-endpoint:
{
"version": "1",
"default": "deny",
"tools": {
"runsync-endpoint": {
"limits": [
{
"counter": "runsync-endpoint_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}runsync-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 a synchronous job to a Serverless endpoint and wait for the result. Best for tasks completing within 90 seconds. If processing exceeds 90 seconds, the response returns a job ID to poll with get-job-status. Max payload: 20 MB. Results expire after 1 minute. Use the wait parameter to extend the server-side wait up to 5 minutes (300000 ms). 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 runsync-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.
runsync-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 runsync-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 runsync-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.
runsync-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.