"""
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 *