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gretl_ols

opens a local dataset, estimates an OLS model, and requires GUI opening by default.

Part of the Gretl MCP server.

gretl_ols can trigger actions in Gretl MCP, 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 gretl_ols to trigger processes or run actions in Gretl MCP. 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.

gretl_ols 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": {
    "gretl_ols": {
      "limits": [
        {
          "counter": "gretl_ols_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Gretl MCP policy for all 12 tools.

Get this rule live on your own Gretl MCP 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 gretl_ols 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 gretl_ols 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 gretl_ols tool do? +

opens a local dataset, estimates an OLS model, and requires GUI opening by default.. It is categorised as a Execute tool in the Gretl MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on gretl_ols? +

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

What risk level is gretl_ols? +

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

Can I rate-limit gretl_ols? +

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

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

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

Enforce policy on every Gretl MCP tool call.

Deterministic rules across all 12 Gretl MCP 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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