AI agents use update_instance to create or update resources in Linode MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Linode MCP Server environment.
The tool creates or modifies data reversibly without permanently destroying resources, placing it in the Write category. Severity is high because misconfigured instance updates in a production environment could cause service disruptions, resource allocation errors, or unintended configuration changes affecting live infrastructure and dependent services.
From the tool's definition Tool name 'update_instance' and description 'Update a Linode instance' indicate modification of cloud infrastructure configuration. This modifies existing compute resources reversibly (as opposed to deletion).
Documented attack patterns abuse exactly the kind of access update_instance gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Linode MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for update_instance:
{
"version": "1",
"default": "deny",
"tools": {
"update_instance": {
"limits": [
{
"counter": "update_instance_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_instance 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 a Linode instance. It is categorised as a Write tool in the Linode MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Linode MCP Server MCP server in PolicyLayer and add a rule for update_instance: 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 Linode MCP Server. Nothing to install.
update_instance 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_instance 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_instance. 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_instance is provided by the Linode MCP Server MCP server (takashito/linode-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Linode MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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416 Linode MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.