This record as markdown: /tools/io-github-devopam-mcpg/schedule-autowarm.md
What schedule_autowarm does on Mcpg
AI agents invoke schedule_autowarm to trigger actions in Mcpg. 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 schedule_autowarm is rated High
The tool name suggests scheduling an automatic warming operation (likely cache or index pre-warming), which is an external operation trigger. With no description, confidence is low. Scheduling/executing background operations falls under Execute. Severity is medium as misuse could cause resource contention but is not directly destructive.
From the tool's definition Tool name 'schedule_autowarm' and empty description; 'schedule' implies triggering an operation, 'autowarm' suggests cache/index warming execution
Attacks that exploit this kind of access
The rule that runs schedule_autowarm safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcpg, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For schedule_autowarm, this is the rule to start with:
schedule_autowarm 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 Mcpg, apply this rule, and every schedule_autowarm call is checked against it from then on.
Questions about schedule_autowarm
schedule_autowarm is a execute tool on the Mcpg MCP server. It is categorised as a Execute tool in the Mcpg MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Mcpg MCP server in PolicyLayer and add a rule for schedule_autowarm: 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 Mcpg. Nothing to install.
schedule_autowarm 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 schedule_autowarm 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 schedule_autowarm. 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.
schedule_autowarm is provided by the Mcpg MCP server (pypi:mcpg). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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