Is your feature request related to a problem? Please describe.
Currently, sktime-mcp has excellent tools for data loading (data_tools), model instantiation, and evaluation (evaluate.py), but it lacks a tool for an agent to natively query the statistical characteristics of a loaded time series (such as stationarity, trend, and seasonality). In agentic workflows, an LLM often needs to analyze a time series before deciding which transformations or forecasting estimators to build.
Describe the solution you'd like
I propose adding an analyze_data.py (or describe_data.py) tool. Given a data_handle, this tool would return a statistical summary including:
- Series length and frequency
- Stationarity (via
adfuller test)
- Presence of trend (e.g., using linear regression slope)
- Presence and strength of seasonality (e.g., using ACF)
Describe alternatives you've considered
Agents currently have to blindly guess data properties or ask the user to manually compute and provide these statistics.
Additional context
I am currently working on a custom agentic forecaster for the sktime ESoC 2026 track, and I have already written the logic to compute these statistics locally using standard sktime/statsmodels checks. I would love to contribute this as a new MCP tool via a Pull Request.
Is your feature request related to a problem? Please describe.
Currently,
sktime-mcphas excellent tools for data loading (data_tools), model instantiation, and evaluation (evaluate.py), but it lacks a tool for an agent to natively query the statistical characteristics of a loaded time series (such as stationarity, trend, and seasonality). In agentic workflows, an LLM often needs to analyze a time series before deciding which transformations or forecasting estimators to build.Describe the solution you'd like
I propose adding an
analyze_data.py(ordescribe_data.py) tool. Given adata_handle, this tool would return a statistical summary including:adfullertest)Describe alternatives you've considered
Agents currently have to blindly guess data properties or ask the user to manually compute and provide these statistics.
Additional context
I am currently working on a custom agentic forecaster for the sktime ESoC 2026 track, and I have already written the logic to compute these statistics locally using standard sktime/statsmodels checks. I would love to contribute this as a new MCP tool via a Pull Request.