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run_benchmark

Execute benchmark tasks for AI evaluation

Risk signalsRuns compute-intensive benchmarks

Part of the Project Management AI Analysis server.

run_benchmark can trigger actions in Project Management AI Analysis, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents invoke run_benchmark to trigger processes or run actions in Project Management AI Analysis. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.

run_benchmark can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.

Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "run_benchmark": {
      "limits": [
        {
          "counter": "run_benchmark_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Project Management AI Analysis policy for all 16 tools.

Get this rule live on your own Project Management AI Analysis server in minutes. PolicyLayer enforces it on every call, before it runs.

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View all 16 tools →

These attack patterns abuse exactly the kind of access run_benchmark gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so run_benchmark only ever does what you allow.

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Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the run_benchmark tool do? +

Execute benchmark tasks for AI evaluation. It is categorised as a Execute tool in the Project Management AI Analysis MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on run_benchmark? +

Register the Project Management AI Analysis MCP server in PolicyLayer and add a rule for run_benchmark: 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 Project Management AI Analysis. Nothing to install.

What risk level is run_benchmark? +

run_benchmark is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit run_benchmark? +

Yes. Add a rate_limit block to the run_benchmark 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 run_benchmark completely? +

Set action: deny in the PolicyLayer policy for run_benchmark. 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 run_benchmark? +

run_benchmark is provided by the Project Management AI Analysis MCP server (pm-mcp-servers). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Project Management AI Analysis tool call.

Deterministic rules across all 16 Project Management AI Analysis tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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