benchmark_run
Запустить скрипт N раз, измерить время.
This record as markdown: /tools/llm-agent-sniffer-integration/benchmark-run.md
What benchmark_run does on Llm Agent Sniffer Integration
AI agents invoke benchmark_run to trigger actions in Llm Agent Sniffer Integration. 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 benchmark_run is rated High
benchmark_run triggers real processes with real consequences. An agent gone sideways doesn't fire it once. It starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Attacks that exploit this kind of access
The rule that runs benchmark_run safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Llm Agent Sniffer Integration, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For benchmark_run, this is the rule to start with:
benchmark_run 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 Llm Agent Sniffer Integration, apply this rule, and every benchmark_run call is checked against it from then on.
Questions about benchmark_run
Запустить скрипт N раз, измерить время. It is categorised as a Execute tool in the Llm Agent Sniffer Integration MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Llm Agent Sniffer Integration MCP server in PolicyLayer and add a rule for benchmark_run: 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 Llm Agent Sniffer Integration. Nothing to install.
benchmark_run 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 benchmark_run 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 benchmark_run. 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.
benchmark_run is provided by the Llm Agent Sniffer Integration MCP server (mobstardos/llm-agent-sniffer-integration). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
More on Llm Agent Sniffer Integration, and thousands of servers like it.
Across the catalogue