MCP Tabular Data Analysis Server

Instructions

Merge/join two datasets together - essential for combining data sources.

Args:
    file_path_left: Path to left/primary dataset
    file_path_right: Path to right/secondary dataset
    on: Column(s) to join on (if same name in both datasets)
    left_on: Column name in left dataset to join on
    right_on: Column name in right dataset to join on
    how: Join type - 'inner', 'left', 'right', 'outer'
    preview_limit: Number of rows to return in preview

Returns:
    Dictionary containing merged data preview and statistics

Implementation Reference

• src/mcp_tabular/server.py:1117-1196 (handler)

  The main handler function for the 'merge_datasets' tool. It is registered via the @mcp.tool() decorator. Loads two datasets using _load_data helper, performs a pandas merge based on specified keys and join type, computes merge statistics, and returns a preview of the merged data with metadata.

  @mcp.tool()
  def merge_datasets(
      file_path_left: str,
      file_path_right: str,
      on: list[str] | None = None,
      left_on: str | None = None,
      right_on: str | None = None,
      how: str = "inner",
      preview_limit: int = 50,
  ) -> dict[str, Any]:
      """
      Merge/join two datasets together - essential for combining data sources.
      
      Args:
          file_path_left: Path to left/primary dataset
          file_path_right: Path to right/secondary dataset
          on: Column(s) to join on (if same name in both datasets)
          left_on: Column name in left dataset to join on
          right_on: Column name in right dataset to join on
          how: Join type - 'inner', 'left', 'right', 'outer'
          preview_limit: Number of rows to return in preview
      
      Returns:
          Dictionary containing merged data preview and statistics
      """
      df_left = _load_data(file_path_left)
      df_right = _load_data(file_path_right)
      
      valid_how = ['inner', 'left', 'right', 'outer']
      if how not in valid_how:
          raise ValueError(f"Unknown join type: {how}. Use: {valid_how}")
      
      # Determine join keys
      if on:
          invalid_left = [c for c in on if c not in df_left.columns]
          invalid_right = [c for c in on if c not in df_right.columns]
          if invalid_left:
              raise ValueError(f"Columns {invalid_left} not found in left dataset")
          if invalid_right:
              raise ValueError(f"Columns {invalid_right} not found in right dataset")
          merged = pd.merge(df_left, df_right, on=on, how=how)
      elif left_on and right_on:
          if left_on not in df_left.columns:
              raise ValueError(f"Column '{left_on}' not found in left dataset")
          if right_on not in df_right.columns:
              raise ValueError(f"Column '{right_on}' not found in right dataset")
          merged = pd.merge(df_left, df_right, left_on=left_on, right_on=right_on, how=how)
      else:
          # Try to find common columns
          common_cols = list(set(df_left.columns) & set(df_right.columns))
          if not common_cols:
              raise ValueError("No common columns found. Specify 'on', or 'left_on' and 'right_on'")
          on = common_cols[:1]  # Use first common column
          merged = pd.merge(df_left, df_right, on=on, how=how)
      
      # Statistics about the merge
      left_rows = len(df_left)
      right_rows = len(df_right)
      merged_rows = len(merged)
      
      merge_stats = {
          "left_rows": left_rows,
          "right_rows": right_rows,
          "merged_rows": merged_rows,
          "join_type": how,
          "join_keys": on if on else {"left": left_on, "right": right_on},
      }
      
      if how == "inner":
          merge_stats["left_match_pct"] = round(merged_rows / left_rows * 100, 1) if left_rows > 0 else 0
          merge_stats["right_match_pct"] = round(merged_rows / right_rows * 100, 1) if right_rows > 0 else 0
      
      return {
          "merge_stats": merge_stats,
          "merged_columns": merged.columns.tolist(),
          "merged_shape": {"rows": merged_rows, "columns": len(merged.columns)},
          "preview": merged.head(preview_limit).to_dict(orient="records"),
          "has_more": merged_rows > preview_limit,
      }