Medium Risk

smart_select_tools

LLM-powered tool selection: sends your task description + a compact tool catalog to a fast model (Gemini 3 Flash, GPT-5-mini, or Claude Haiku 4.5) to pick the best 5-10 tools. Much more accurate than keyword search for ambiguous queries like

Part of the Nodebench server.

smart_select_tools can modify Nodebench data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use smart_select_tools to create or modify resources in Nodebench. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call smart_select_tools repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Nodebench.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "smart_select_tools": {
      "limits": [
        {
          "counter": "smart_select_tools_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Nodebench policy for all 724 tools.

Get this rule live on your own Nodebench server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access smart_select_tools 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 smart_select_tools only ever does what you allow.

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

What does the smart_select_tools tool do? +

LLM-powered tool selection: sends your task description + a compact tool catalog to a fast model (Gemini 3 Flash, GPT-5-mini, or Claude Haiku 4.5) to pick the best 5-10 tools. Much more accurate than keyword search for ambiguous queries like. It is categorised as a Write tool in the Nodebench MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on smart_select_tools? +

Register the Nodebench MCP server in PolicyLayer and add a rule for smart_select_tools: 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 Nodebench. Nothing to install.

What risk level is smart_select_tools? +

smart_select_tools is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit smart_select_tools? +

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

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

smart_select_tools is provided by the Nodebench MCP server (nodebench-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Nodebench tool call.

Deterministic rules across all 724 Nodebench 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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