Fit a time series regressor on training data and predict continuous target values on test data. Use with regression estimators like TimeSeriesForestRegressor, etc.
AI agents invoke fit_predict_regression to trigger actions in Sktime. 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.
This tool executes a machine learning workflow: it trains (fits) a regression model on provided data and runs inference (predict) on test data. This involves running computational processes whose effects depend on the input arguments (model type, training data, test data). It is an Execute category action as it triggers an external ML computation pipeline.
From the tool's definition Fit a time series regressor on training data and predict continuous target values on test data
Documented attack patterns abuse exactly the kind of access fit_predict_regression gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Sktime, and nothing reaches the server without passing your rules. This is the rule we recommend for fit_predict_regression:
{
"version": "1",
"default": "deny",
"tools": {
"fit_predict_regression": {
"limits": [
{
"counter": "fit_predict_regression_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}fit_predict_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.
Free to start. No card required.
Fit a time series regressor on training data and predict continuous target values on test data. Use with regression estimators like TimeSeriesForestRegressor, etc. It is categorised as a Execute tool in the Sktime MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Sktime MCP server in PolicyLayer and add a rule for fit_predict_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 Sktime. Nothing to install.
fit_predict_regression is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the fit_predict_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.
Set action: deny in the PolicyLayer policy for fit_predict_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.
fit_predict_regression is provided by the Sktime MCP server (sktime/sktime-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Sktime, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
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