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MCP tools for dataset discovery, inspection, and contrast validation.
These tools give the LLM agent a structured way to:
1. Discover what datasets are registered (dataset_list_available)
2. Understand a dataset before loading it (dataset_describe)
3. Survey a loaded metadata file for usable grouping columns
(dataset_list_valid_sample_groups)
4. Validate a DE contrast before running analysis
(dataset_validate_contrast)
Design rules
------------
- All dataset-specific knowledge lives in YAML manifests under
src/datasets/manifests/. These tools read those manifests; they do not
hard-code any dataset logic.
- Workflow logic (group usability, contrast validation) lives in
src/workflows/metadata_validation.py, not here.
- All return values are JSON-serialisable dicts.
- Tool docstrings are written for the LLM agent, not for human readers.
They explain WHEN to call the tool and what to do next.
"""
# ruff: noqa: F401, F403
from ._base import *
from ._base import (
_build_loading_plan,
_classify_metadata_values,
_col_semantics,
_detect_intent,
_flatten_single_column_summary,
_load_metadata_df,
_workflow_for_intent,
)
from .catalog import *
from .metadata import *
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