Core Concepts

sktime-mcp is a specialized interface that allows you to perform advanced time-series analysis by collaborating with an AI assistant. It bridges the gap between natural language requests and the rigorous execution environment of the sktime library.

Collaborative Model Discovery

Instead of requiring you to know the exact names of hundreds of forecasting models, sktime-mcp enables a discovery-based workflow. You can ask your assistant to find models based on your data’s characteristics:

  • “Find models for multivariate data”

  • “Which estimators handle missing values?”

  • “Show me probabilistic forecasters.”

The system queries the sktime registry in real-time, ensuring your assistant always has access to the most up-to-date models and their metadata.

Stateful Interaction (Handles)

To allow for complex, multi-turn conversations, the server maintains a stateful runtime. This is managed through Handles.

How it works for you

When you ask the assistant to load a dataset or create a model, the server creates that object in memory and assigns it a “handle” (a unique ID).

  • You don’t need to track these IDs yourself.

  • You can refer to objects naturally: “Use the model we just created” or “Run that forecast on the sales data I loaded earlier.”

  • The assistant manages the handles behind the scenes to ensure your requests are executed on the correct objects.

Memory Management

Because these objects stay in memory to support follow-up questions, you can tell the assistant to “clear the session” or “release the data” when you are finished to free up system resources.

Asynchronous Background Jobs

Time-series work can be computationally intensive. If a task (like training a deep learning model) will take a long time, you can ask the assistant to run it in the background.

Under the hood this is a single flag — run_async: true — accepted by the four tools that can be slow: fit, predict, evaluate, and load_data_source. There are no separate “async” tools. The call returns a job ID straight away instead of a result.

  • This allows you to continue the conversation while the model trains.

  • You can ask for a status update at any time: “Is the model training finished yet?” — the assistant checks the job for you.

  • You can also cancel work that is taking too long: “Stop that training run.”

Note

Nothing arrives unprompted. The server has no way to push a message to you when a job finishes, so the assistant only learns the job is done when it checks. Ask if you want to know.

See the MCP Tool Reference for the job tools and the exact flag.

Safety and Reproducibility

Execution Model

For modelling, sktime-mcp does not ask an LLM to write Python and then run it. Your assistant selects from a fixed set of tools with validated inputs, and models are constructed through sktime’s own registry — so a typo or a hallucinated class name fails cleanly instead of executing something unintended.

Warning

One tool is deliberately outside that boundary. run_command runs an arbitrary shell command with your account’s privileges, so the assistant can install a missing optional dependency mid-session. It is powerful and it is not sandboxed.

Most MCP clients let you approve or deny tools individually. Keep run_command on ask-every-time, or turn it off, unless you actively want the assistant installing packages and inspecting files for you. See MCP Tool Reference.

The server has no authentication layer of its own: it runs as you, with your filesystem permissions. Treat it as a local trusted tool, and use a container if you want a harder boundary.

From Conversation to Code

Once you’ve found a workflow that works, you can turn your conversation into a permanent asset. Ask the assistant to “export the Python code,” and it will generate a standalone script that reproduces your entire analysis exactly.