Set nozzle or bed temperature via G-code. Validates against safe limits.
AI agents invoke set_temperature to trigger actions in Bambu Lab 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 G-code on a physical 3D printer to change temperature of nozzle or heated bed. While it validates against 'safe limits,' it directly controls hardware that can cause fire, burns, or equipment damage if misused or if limits are bypassed/misconfigured.
From the tool's definition 'Set nozzle or bed temperature via G-code' — sends G-code commands to physical hardware to change thermal settings
Documented attack patterns abuse exactly the kind of access set_temperature gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Bambu Lab MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for set_temperature:
{
"version": "1",
"default": "deny",
"tools": {
"set_temperature": {
"limits": [
{
"counter": "set_temperature_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}set_temperature 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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Set nozzle or bed temperature via G-code. Validates against safe limits. It is categorised as a Execute tool in the Bambu Lab MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Bambu Lab MCP Server MCP server in PolicyLayer and add a rule for set_temperature: 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 Bambu Lab MCP Server. Nothing to install.
set_temperature 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 set_temperature 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 set_temperature. 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.
set_temperature is provided by the Bambu Lab MCP Server MCP server (schwarztim/bambu-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Bambu Lab MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
31 Bambu Lab MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.