AI agents use update-endpoint to create or update resources in RunPod MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your RunPod MCP Server environment.
Updates modify configuration state reversibly, fitting the Write category. Severity is high because misconfigured endpoints in a cloud compute platform could disrupt services, expose resources, or alter access controls. Confidence is slightly reduced due to the absent description, but the naming convention and sibling tools provide sufficient context.
From the tool's definition Tool name 'update-endpoint' indicates modification of endpoint configuration. Sibling tools include 'create-endpoint' and 'delete-endpoint', establishing this server's pattern of endpoint lifecycle management.
Documented attack patterns abuse exactly the kind of access update-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 update-endpoint:
{
"version": "1",
"default": "deny",
"tools": {
"update-endpoint": {
"limits": [
{
"counter": "update-endpoint_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update-endpoint 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.
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update-endpoint. It is categorised as a Write tool in the RunPod MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the RunPod MCP Server MCP server in PolicyLayer and add a rule for update-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.
update-endpoint is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the update-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 update-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.
update-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.
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36 RunPod MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.