Installation

This guide covers the steps required to install sktime-mcp for both end-users and developers.

Prerequisites

Python: Version 3.10 or higher.
pip: The Python package installer.
MCP Client: A compatible Model Context Protocol client such as Claude Desktop, Cursor, or a VS Code extension (e.g., Cline).

Standard Installation

The recommended way to install sktime-mcp is via pip or uv. To enable full functionality, including support for various data formats and forecasting models, use the [all] extra.

Using pip

pip install "sktime-mcp[all]"

Using uv

If you prefer uv, you can run the server directly without manual installation using uvx:

uvx sktime-mcp

Or install it globally:

uv tool install "sktime-mcp[all]"

Optional Extras

Depending on your use case, you may choose to install specific dependency groups:

Extra	Description
`forecasting`	Extended forecasting models (Prophet, TBATS, StatsForecast).
`sql`	Support for loading data from SQL databases (PostgreSQL, MySQL).
`files`	Support for Excel and Parquet file formats.
`mlflow`	Integration with MLflow for model persistence.
`dl`	Deep learning models (TensorFlow, PyTorch).

To install a specific extra, use:

pip install "sktime-mcp[extra_name]"

Developer Installation

If you intend to contribute to the project or run the test suite, install sktime-mcp from source in editable mode.

Clone the repository:

git clone https://github.com/sktime/sktime-mcp.git
cd sktime-mcp

Create and activate a virtual environment:

python3 -m venv .venv
source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

Install in editable mode with development dependencies:
```
pip install -e ".[dev,all]"
```

Verification

After installation, verify that the sktime-mcp CLI tool is available by checking its version:

sktime-mcp --version

If the command is not found, ensure that your Python scripts directory is in your system’s PATH.