sktime-mcp

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MCP (Model Context Protocol) layer for sktime - Registry-Driven for LLMs

A semantic engine that exposes sktime's native registry and semantics to Large Language Models, enabling them to:

• 🔍 Discover valid estimators
• 🧠 Reason about estimator capabilities
• 🔗 Compose compatible estimators
• ⚡ Execute real sktime workflows on real data

🎯 Design Philosophy

This MCP is not just documentation or static code analysis. It is a semantic engine for programmatic model usage.

Key Principles

1. sktime as Source of Truth - No AST parsing, no repo indexing, no heuristics. All structure comes from all_estimators, estimator tags, and sktime's API contracts.

2. Registry-First - Instead of File → Class → Infer Relationships, we do Registry → Semantics → Safe Execution.

3. Minimal MCP Surface - Exposes only what an LLM needs: Discovery, Description, Instantiation, Execution, and model persistence.

🛠️ Installation

Zero-install via uvx (recommended)

If you have uv installed, no separate installation step is needed. Just update your MCP client config (see Connecting from an LLM Client below) and uvx will handle the rest automatically.

# Verify uv is available
uvx sktime-mcp --help

pip

pip install sktime-mcp

# With optional extras (SQL, forecasting models, file formats)
pip install "sktime-mcp[all]"

Development installation

git clone https://github.com/sktime/sktime-mcp
cd sktime-mcp
python3 -m pip install -e ".[dev]"

🐳 Docker

Run without installing anything locally (only Docker required):

# Build the image
docker build -t sktime-mcp .

# Run the MCP server (stdio transport)
docker run -i sktime-mcp

Or use Docker Compose:

docker compose build
docker compose run sktime-mcp

Claude Desktop — use Docker as the MCP server command:

{
  "mcpServers": {
    "sktime": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "sktime-mcp"]
    }
  }
}

Environment variables can be passed at runtime:

docker run -i -e SKTIME_MCP_LOG_LEVEL=DEBUG sktime-mcp

For a more detailed first-time setup flow, including MCP server verification and troubleshooting, see Beginner Setup.

🧭 Beginner Setup (First‑Time Users)

If you are new to sktime‑mcp or to MCP‑based workflows, this section provides a minimal starting point to help you verify that your setup is working correctly.

What is MCP?

The Model Context Protocol (MCP) allows Large Language Models (LLMs) to discover, reason about, and execute sktime workflows programmatically. This project exposes sktime’s estimator registry and semantics in a structured way so that LLMs can safely compose and run real time‑series pipelines.

Prerequisites

• Python 3.10 or newer
• A working Python virtual environment (recommended)
• pip installed

macOS / Unix-like first-time setup

For macOS or Unix-like shells, create an isolated virtual environment before installing the package:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install sktime-mcp

For development (if you want to modify the source):

python -m pip install -e ".[dev]"

Verify that the MCP server starts:

sktime-mcp

If the sktime-mcp console command is not found (e.g. the script was not placed on your PATH), use the module fallback instead — this is also the recommended form when an MCP client needs to target a specific Python environment:

python -m sktime_mcp.server

Common first-time issues:

Symptom	Likely cause	Fix
command not found: sktime-mcp	Scripts directory not on PATH	Run python -m sktime_mcp.server or add .venv/bin to your PATH
ModuleNotFoundError: sktime_mcp	Package not installed in the active environment	Confirm .venv is active (which python) and re-run pip install sktime-mcp
pip: command not found	System pip not available	Use python -m pip instead of bare pip
Wrong Python version selected	Multiple Python installations	Invoke python3 -m venv .venv explicitly and always use python inside the activated environment

Minimal Setup Check

After completing the steps above, confirm the server starts with sktime-mcp. See the macOS / Unix-like first-time setup section for the fallback command and common error solutions.

Note: On Windows, the sktime-mcp command may be installed to a directory not on your PATH (e.g., %APPDATA%\Python\Python3xx\Scripts). Either add that directory to your PATH or use python -m sktime_mcp.server instead.

🚀 Quick Start

Running the MCP Server

Standard Stdio Mode (for MCP Clients)

sktime-mcp

HTTP/SSE Mode via FastAPI (for Web Browsers or ChatGPT)

To expose the MCP server as a REST API over SSE (Server-Sent Events) for direct consumption:

PYTHONPATH=src .venv/bin/uvicorn sktime_mcp.app:app --host 127.0.0.1 --port 8001

This exposes standard SSE on /sse and message passing on /messages/.

Note for ChatGPT Web Users: ChatGPT runs in the cloud and cannot connect to http://127.0.0.1 (you will get an "Unsafe URL" error). You must expose your local server to the internet using a secure tunnel like ngrok:

ngrok http 8001

Then use the provided https://<your-ngrok-id>.ngrok-free.app/sse URL in ChatGPT.

Configuration (Environment Variables)

You can configure the server's behavior at runtime using environment variables:

• SKTIME_MCP_MAX_RESPONSE_TOKENS: Maximum tokens allowed per tool response (e.g., 10000). If a response exceeds this limit, it is truncated and appended with a notice. Set to 0 (default) for unlimited.
• SKTIME_MCP_LOG_LEVEL: Server logging verbosity level (DEBUG, INFO, WARNING, ERROR). Defaults to WARNING.
• SKTIME_MCP_AUTO_FORMAT: Enables or disables automatic time-series formatting during data loading.
• SKTIME_MCP_JOB_MAX_AGE_HOURS: Maximum hours before completed background jobs are automatically pruned. Defaults to 24.

Connecting from an LLM Client

The server uses stdio transport by default, compatible with Claude Desktop, Claude Code, and other MCP clients.

Claude Desktop — add to your config file:

Platform	Config path
macOS	~/Library/Application Support/Claude/claude_desktop_config.json
Linux	~/.config/claude/claude_desktop_config.json
Windows	%APPDATA%\Claude\claude_desktop_config.json

With uvx (recommended — no prior install needed):

{
  "mcpServers": {
    "sktime": {
      "command": "uvx",
      "args": ["sktime-mcp"]
    }
  }
}

With optional extras:

{
  "mcpServers": {
    "sktime": {
      "command": "uvx",
      "args": ["sktime-mcp[forecasting,sql]"]
    }
  }
}

With pip-installed package:

{
  "mcpServers": {
    "sktime": {
      "command": "sktime-mcp"
    }
  }
}

⚙️ Configuration

The server can be configured via environment variables:

Environment Variable	Description	Default
SKTIME_MCP_LOG_LEVEL	Logging verbosity (e.g. INFO, DEBUG, WARNING)	"WARNING"
SKTIME_MCP_LOG_PATH	Optional file path to output logs to in addition to stderr	(None)
SKTIME_MCP_AUTO_FORMAT	Automatically format time series data on load (true/false)	"true"
SKTIME_MCP_JOB_MAX_AGE_HOURS	Maximum age in hours before background jobs are cleared	24
SKTIME_MCP_JOB_CLEANUP_INTERVAL	Interval in seconds for periodic job cleanup checks	3600

📚 Available Tools

The full tool reference is in the project documentation: https://sktime.github.io/sktime-mcp/

Need	Tool options	Rough explanation
Discover what sktime can do	list_available_data, query_registry, describe_component	Find demo data, estimators, tags, and component details.
Bring data into the session	load_data_source, inspect_data, transform_data, split_data, save_data	Load files, inline data, SQL, or URLs into handles; inspect, clean, split, and persist them.
Build and run models	instantiate_estimator, fit, predict, update, get_fitted_params, call_method	Create sktime estimators or pipelines, fit them, forecast, update, or call native methods.
Evaluate and reproduce	evaluate_estimator, export_code, save_model, load_model	Cross-validate, generate Python code, and persist fitted models.
Manage runtime state	list_handles, release_handle, release_data_handle, list_jobs, check_job_status, cancel_job	See what is in memory, clean it up, and track async work.

The practical mental model is simple: prompts create tool calls, tool calls create handles, and handles let later prompts continue the workflow.

🔄 Example LLM Flows

See the User Guide for end-to-end workflow examples, including:

• Discovering sktime coverage
• Retail forecasting and saving results
• Cleaning messy business data
• Time-series classification

📁 Project Structure

sktime-mcp/
├── src/sktime_mcp/
│   ├── server.py           # MCP server entry point
│   ├── registry/           # Registry interface & tag resolver
│   ├── composition/        # Pipeline composition validator
│   ├── runtime/            # Execution engine, handle & job management
│   ├── data/               # Data adapters (file, pandas, SQL, URL)
│   └── tools/              # MCP tool implementations
├── docs/                   # Sphinx documentation source
├── examples/               # Usage examples
├── tests/                  # Test suite
├── Dockerfile              # Multi-stage container build
├── docker-compose.yml      # Compose service definition
└── .dockerignore           # Docker build context filter

🧪 Running Tests

pytest tests/

Local Quality Checks

Run standardized local checks before raising a PR:

make check

Auto-fix formatting and fixable lint issues:

make format-fix

If make is unavailable (common on Windows), run the equivalent commands:

ruff format --check .
ruff check .
pytest

Pre-Commit Hooks (Recommended)

To ensure your code meets quality standards before pushing, install the pre-commit hooks:

make install-hooks

This will automatically run Ruff and Pytest on your code every time you make a commit.