predict_node_regression · Neo4j Gds MCP: Risk & Policy

WHAT IT DOES

What predict_node_regression does on Neo4j Gds

AI agents invoke predict_node_regression to trigger actions in Neo4j Gds. 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 predict_node_regression needs a policy

This tool executes a machine learning prediction pipeline on the Neo4j graph database using a trained model from the model catalog. It runs a computational process (ML inference) and produces results, fitting the Execute category. It doesn't merely read static data — it triggers algorithm execution.

From the tool's definition 'Predict numeric values for nodes in a projected graph using a trained node regression model' — triggers ML inference execution against the database using a stored model

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

predict_node_regression Rate-limited

predict_node_regression 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": {
    "predict_node_regression": {
      "limits": [
        {
          "counter": "predict_node_regression_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 Neo4j Gds, so you can run agents against it safely.

FAQ

Questions about predict_node_regression

What does the predict_node_regression tool do? +

Predict numeric values for nodes in a projected graph using a trained node regression model from the model catalog. It is categorised as a Execute tool in the Neo4j Gds MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on predict_node_regression? +

Register the Neo4j Gds MCP server in PolicyLayer and add a rule for predict_node_regression: 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 Neo4j Gds. Nothing to install.

What risk level is predict_node_regression? +

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

Can I rate-limit predict_node_regression? +

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

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

predict_node_regression is provided by the Neo4j Gds MCP server (neo4j-contrib/gds-agent). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.