feature_importance_tree · Feature Evaluation MCP Server MCP

WHAT IT DOES

What feature_importance_tree does on Feature Evaluation MCP Server

AI agents invoke feature_importance_tree to trigger actions in Feature Evaluation MCP Server. 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 IT NEEDS A POLICY

Why feature_importance_tree needs a policy

This tool executes a tree-based model (e.g., Random Forest, Gradient Boosting) to compute feature importances. It doesn't merely read stored data but actively runs a computational model. It has no destructive, financial, or write side effects, but it does trigger external computation/model execution, placing it in Execute.

From the tool's definition 'Compute feature importance using a tree-based model' — this tool trains/runs a tree-based model on data, which involves executing a machine learning algorithm

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

feature_importance_tree Rate-limited

feature_importance_tree 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "feature_importance_tree": {
      "limits": [
        {
          "counter": "feature_importance_tree_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Feature Evaluation MCP Server, so you can run agents against it safely.

FAQ

Questions about feature_importance_tree

What does the feature_importance_tree tool do? +

Compute feature importance using a tree-based model. It is categorised as a Execute tool in the Feature Evaluation MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on feature_importance_tree? +

Register the Feature Evaluation MCP Server MCP server in PolicyLayer and add a rule for feature_importance_tree: 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 Feature Evaluation MCP Server. Nothing to install.

What risk level is feature_importance_tree? +

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

Can I rate-limit feature_importance_tree? +

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

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

feature_importance_tree is provided by the Feature Evaluation MCP Server MCP server (jaivardhan1209/featureengineering). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.