This record as markdown: /tools/takashito-linode-mcp-server/resize-instance.md
What resize_instance does on Linode MCP Server
AI agents invoke resize_instance to trigger actions in Linode 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.
Why resize_instance is rated High
Resizing a cloud instance is an Execute-class operation: it triggers an external infrastructure action (changing compute resources) that may cause downtime, data migration, and irreversible disk changes depending on resize direction.
From the tool's definition "Resize a Linode instance" — resizing triggers a compute operation that modifies the instance's hardware configuration (CPU, RAM, disk)
Attacks that exploit this kind of access
The rule that runs resize_instance safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Linode MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For resize_instance, this is the rule to start with:
resize_instance 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Linode MCP Server, apply this rule, and every resize_instance call is checked against it from then on.
Questions about resize_instance
Resize a Linode instance. It is categorised as a Execute tool in the Linode MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Linode MCP Server MCP server in PolicyLayer and add a rule for resize_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.
resize_instance 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 resize_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 resize_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.
resize_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.
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