Advanced performance profiling with bottleneck detection and recommendations
AI agents invoke performance_profile to trigger actions in Browser Connect. 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.
Performance profiling requires executing code instrumentation and runtime monitoring that can affect application behavior and resource consumption. While read-only in intent (collecting metrics), profiling operations intrinsically execute instrumentation code and can trigger side effects like CPU/memory overhead, request interception, or code injection.
From the tool's definition Tool performs 'Advanced performance profiling with bottleneck detection' which requires executing profiling operations within a browser or application runtime.
Documented attack patterns abuse exactly the kind of access performance_profile gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Browser Connect, and nothing reaches the server without passing your rules. This is the rule we recommend for performance_profile:
{
"version": "1",
"default": "deny",
"tools": {
"performance_profile": {
"limits": [
{
"counter": "performance_profile_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}performance_profile 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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Advanced performance profiling with bottleneck detection and recommendations. It is categorised as a Execute tool in the Browser Connect MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Browser Connect MCP server in PolicyLayer and add a rule for performance_profile: 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 Browser Connect. Nothing to install.
performance_profile 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 performance_profile 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 performance_profile. 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.
performance_profile is provided by the Browser Connect MCP server (perception30/browser-connect-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Browser Connect, 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.
16 Browser Connect tools catalogued and risk-classified — across an index of 43,000+ MCP servers.