Feature/data management#466
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…save_data - inspect_data: rich metadata (mtype, scitype, shape, freq, cutoff, missing values, head, summary_stats) - split_data: temporal train/test split with test_size (fraction) or fh (horizon count) - transform_data: unified action='format' (auto-fix freq/dupes/NaN) or action='convert' (mtype conversion) - save_data: persist data handles to CSV/Parquet/JSON files - Added 15 unit tests covering all tools and edge cases - Wired all 4 tools into server.py (Tool schemas + call_tool dispatcher) - Updated tools/__init__.py exports
Add accurate LLM-facing descriptions for inspect_data, split_data, transform_data, and save_data. Remove format_time_series MCP tool now subsumed by transform_data(action='format'). Fix fh list splitting to use max(fh) steps, rename return test_size to n_test, and add fh validation. Co-authored-by: Cursor <cursoragent@cursor.com>
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PR: Add Data Management Tools
Reference Issues/PRs
Fixes #465
Independence: This PR is rebased on latest
mainand does not depend on #463 or any other open PR. It can be merged on its own. (Previously bundled #463 commits; those have been removed so this PR contains only data-management changes.)What does this implement/fix? Explain your changes.
Adds 4 new MCP tools for the Data Management group:
inspect_datamtype(),check_is_mtype(),get_cutoff(), scitype detectionsplit_datatest_size(fraction) orfh(horizon count)temporal_train_test_split()transform_dataaction="format"(auto-fix freq/dupes/NaN) oraction="convert"(mtype conversion)convert_to(), frequency inference, fill NaN, dedupsave_dataFiles created:
src/sktime_mcp/tools/inspect_data.pysrc/sktime_mcp/tools/split_data.pysrc/sktime_mcp/tools/transform_data.pysrc/sktime_mcp/tools/save_data.pytests/test_data_management.pyFiles modified:
src/sktime_mcp/server.py— tool schemas + dispatchersrc/sktime_mcp/tools/__init__.py— exportsREADME.md— tool documentationThe existing
format_time_seriestool is preserved for backward compatibility;transform_data(action="format")delegates to the same executor logic.Does your contribution introduce a new dependency? If yes, which one?
No. Uses existing dependencies (
pandas,sktime).What should a reviewer concentrate their feedback on?
inspect_data— mtype/scitype detection fallback logicsplit_data— temporal splitting and handle registrationtransform_data—convert_to()edge casessave_data— format dispatch and path handlingAny other comments
Rebased on latest
main(3 commits, +1282 / −31). 197 tests pass undermake check.PR checklist
For all contributions
make check).docs/source/.