Fit a time series classifier on training data and predict class labels on test data. Use with classification estimators like RocketClassifier, HIVECOTEV2, etc.
AI agents invoke fit_predict_classification 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.
The tool executes a machine learning workflow (fit + predict) on provided data. This is an Execute-category action: it runs computation and produces outputs, but does not delete data, move money, or irreversibly modify state. Misuse could cause incorrect predictions or waste compute resources, hence medium severity.
From the tool's definition 'Fit a time series classifier on training data and predict class labels on test data' — trains a model and runs predictions, triggering external ML computation whose effects depend on the estimator and data provided.
Documented attack patterns abuse exactly the kind of access fit_predict_classification 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_classification:
{
"version": "1",
"default": "deny",
"tools": {
"fit_predict_classification": {
"limits": [
{
"counter": "fit_predict_classification_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}fit_predict_classification 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 classifier on training data and predict class labels on test data. Use with classification estimators like RocketClassifier, HIVECOTEV2, 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_classification: 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_classification 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_classification 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_classification. 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_classification 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.
24 Sktime tools catalogued and risk-classified — across an index of 43,000+ MCP servers.