This record as markdown: /tools/hybridlabor-api-aos/optimize-performance.md
What optimize_performance does on Aos
AI agents invoke optimize_performance to trigger actions in Aos. 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 optimize_performance is rated High
Modifies system configuration and resource settings with externally-triggered effects based on scan results.
From the tool's definition Scan network, fix bottlenecks, lowers resolution of flagged TOPs.
Attacks that exploit this kind of access
The rule that runs optimize_performance safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Aos, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For optimize_performance, this is the rule to start with:
optimize_performance 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 Aos, apply this rule, and every optimize_performance call is checked against it from then on.
Questions about optimize_performance
Scan a network for cook-time bottlenecks and (optionally) fix them. By default it reports the slowest nodes with a concrete suggestion each. With apply:true it lowers the resolution of the flagged TOPs by. It is categorised as a Execute tool in the Aos MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Aos MCP server in PolicyLayer and add a rule for optimize_performance: 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 Aos. Nothing to install.
optimize_performance 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 optimize_performance 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 optimize_performance. 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.
optimize_performance is provided by the Aos MCP server (@hybridlabor-api/aos). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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