This record as markdown: /tools/io-github-devopam-mcpg/prewarm-recommended.md
What prewarm_recommended does on Mcpg
AI agents invoke prewarm_recommended 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 prewarm_recommended is rated High
The tool name suggests a PostgreSQL cache prewarming operation, which executes a server-side action to load recommended data/indexes into memory. This is not a simple read but an active operation that modifies server memory state. Confidence is low due to empty description.
From the tool's definition Tool name 'prewarm_recommended' and empty description. In PostgreSQL context, 'prewarm' typically refers to loading data into shared buffer cache (pg_prewarm), which is an execution operation affecting server memory state.
Attacks that exploit this kind of access
The rule that runs prewarm_recommended 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 prewarm_recommended, this is the rule to start with:
prewarm_recommended 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 prewarm_recommended call is checked against it from then on.
Questions about prewarm_recommended
prewarm_recommended 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 prewarm_recommended: 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.
prewarm_recommended 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 prewarm_recommended 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 prewarm_recommended. 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.
prewarm_recommended 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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