get_important_features
Identify key features for horse racing prediction, with explanations and practical usage methods to improve race analysis.
Instructions
競馬予測で重要な特徴量の知見を提供
Returns:
重要特徴量のリスト、説明、での活用方法Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- src/jvlink_mcp_server/server.py:276-288 (handler)Handler function for the 'get_important_features' MCP tool. Returns important feature insights for horse racing predictions, including features, combinations, count, and references from FEATURE_IMPORTANCE_DATA.
@mcp.tool() def get_important_features() -> dict: """競馬予測で重要な特徴量の知見を提供 Returns: 重要特徴量のリスト、説明、での活用方法 """ return { "features": FEATURE_IMPORTANCE_DATA["important_features"], "feature_combinations": FEATURE_IMPORTANCE_DATA["feature_combinations"], "total_features": len(FEATURE_IMPORTANCE_DATA["important_features"]), "references": FEATURE_IMPORTANCE_DATA["references"] } - src/jvlink_mcp_server/server.py:276-276 (registration)Registration of the tool using the @mcp.tool() decorator on the get_important_features function.
@mcp.tool() - Return type schema: dict with keys 'features' (list), 'feature_combinations' (list), 'total_features' (int), 'references' (list). No input parameters.
"""競馬予測で重要な特徴量の知見を提供 Returns: 重要特徴量のリスト、説明、での活用方法 """ return { "features": FEATURE_IMPORTANCE_DATA["important_features"], "feature_combinations": FEATURE_IMPORTANCE_DATA["feature_combinations"], "total_features": len(FEATURE_IMPORTANCE_DATA["important_features"]), "references": FEATURE_IMPORTANCE_DATA["references"] } - Data loading helper: loads FEATURE_IMPORTANCE_DATA from data/feature_importance.json file, with fallback to empty defaults if file not found.
_feature_importance_path = DATA_DIR / "feature_importance.json" if _feature_importance_path.exists(): with open(_feature_importance_path, "r", encoding="utf-8") as f: FEATURE_IMPORTANCE_DATA = json.load(f) else: import logging logging.getLogger(__name__).warning( f"feature_importance.json not found at {_feature_importance_path}" ) FEATURE_IMPORTANCE_DATA = { "important_features": [], "feature_combinations": [], "references": [] }