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run_forecast

Run a forecast now. Reserves AI credits up front, dispatches the backtest + projection + correlation pipeline, and returns either the completed result inline (wait_seconds > 0 and the run finishes in time) or a run_id you can poll with get_forecast_run.

Part of the clariBI server.

run_forecast can trigger actions in clariBI, 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_forecast to trigger processes or run actions in clariBI. 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_forecast 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_forecast": {
      "limits": [
        {
          "counter": "run_forecast_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full clariBI policy for all 24 tools.

Get this rule live on your own clariBI 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 run_forecast 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_forecast 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_forecast tool do? +

Run a forecast now. Reserves AI credits up front, dispatches the backtest + projection + correlation pipeline, and returns either the completed result inline (wait_seconds > 0 and the run finishes in time) or a run_id you can poll with get_forecast_run.. It is categorised as a Execute tool in the clariBI 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_forecast? +

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

What risk level is run_forecast? +

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

Can I rate-limit run_forecast? +

Yes. Add a rate_limit block to the run_forecast 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_forecast completely? +

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

run_forecast is provided by the clariBI MCP server (https://claribi.com/mcp/v1/). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every clariBI tool call.

Deterministic rules across all 24 clariBI tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

Free to start. No card required.

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

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