ai_bench

Benchmark a prompt across local Llama AND cloud models side by side. Compare speed, quality, and cost. Cloud calls use real API keys (cost money). Example: ai_bench({ prompt:

SERVER0nmcp SOURCE0nmcp

Critical RISK CLASS

Category Financial

Parameters 00 required

Recommended Approval-gatedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-0nork-0nmcp/ai-bench.md

WHAT IT DOES

What ai_bench does on 0nmcp

AI agents use ai_bench to commit financial operations through 0nmcp, usually the final step of a payment, billing, or trading workflow. A call moves real money.

RISK

Why ai_bench is rated Critical

The tool explicitly states that cloud API calls cost money, meaning it incurs real financial charges when invoked. Even though its primary purpose is benchmarking/comparison, the financial side-effect is explicitly noted and unavoidable when cloud models are used. Financial > all other categories per the rules.

From the tool's definition Cloud calls use real API keys (cost money)

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs ai_bench safely

PolicyLayer is an MCP gateway: it sits between your AI agents and 0nmcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ai_bench, this is the rule to start with:

ai_bench Approval-gated

Any call to ai_bench is blocked until a human approves it. The rest of the server keeps working.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "ai_bench": {
      "deny_if": [
        {
          "conditions": [],
          "on_deny": "Requires human approval."
        }
      ]
    }
  }
}

GATE THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect 0nmcp, apply this rule, and every ai_bench call is checked against it from then on.

FAQ

Questions about ai_bench

What does the ai_bench tool do? +

Benchmark a prompt across local Llama AND cloud models side by side. Compare speed, quality, and cost. Cloud calls use real API keys (cost money). Example: ai_bench({ prompt:. It is categorised as a Financial tool in the 0nmcp MCP Server, which means it involves financial transactions. Block by default and require explicit approval.

How do I enforce a policy on ai_bench? +

Register the 0n MCP server in PolicyLayer and add a rule for ai_bench: 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 0nmcp. Nothing to install.

What risk level is ai_bench? +

ai_bench is a Financial tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.

Can I rate-limit ai_bench? +

Yes. Add a rate_limit block to the ai_bench 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.

How do I block ai_bench completely? +

Set action: deny in the PolicyLayer policy for ai_bench. 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.

What MCP server provides ai_bench? +

ai_bench is provided by the 0n MCP server (0nmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

More on 0n, and thousands of servers like it.

This server

All 218 0n tools → Critical-risk tools on this server →

Across the catalogue

All critical-risk MCP tools → The MCP Attack Database →

Guides

Govern MCP payment tools →Human-in-the-loop →Tool call approval →