create_correlation_heatmap
Visualize correlations between numeric columns in a dataset by generating a heatmap image. Specify columns to analyze and save the visualization to a file.
Instructions
Create a correlation heatmap from numeric columns in the dataset.
Args: file_path: Path to the data file output_path: Path where to save the heatmap image columns: Optional list of specific columns to include (if None, uses all numeric columns)
Returns: Information about the created heatmap
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | ||
| output_path | Yes | ||
| columns | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/visidata_mcp/server.py:687-765 (handler)The main handler function that creates a correlation heatmap from numeric columns in a dataset. It loads data using pandas, selects numeric columns (or user-specified columns), calculates the correlation matrix, and creates a visualization using seaborn with matplotlib. The function saves the heatmap to the specified output path and returns metadata about the created visualization.
@mcp.tool() def create_correlation_heatmap(file_path: str, output_path: str, columns: Optional[List[str]] = None) -> str: """ Create a correlation heatmap from numeric columns in the dataset. Args: file_path: Path to the data file output_path: Path where to save the heatmap image columns: Optional list of specific columns to include (if None, uses all numeric columns) Returns: Information about the created heatmap """ try: if not VISUALIZATION_AVAILABLE: return f"Error: {VISUALIZATION_ERROR}" import pandas as pd from pathlib import Path # Load the data file_extension = Path(file_path).suffix.lower() if file_extension == '.csv': df = pd.read_csv(file_path) elif file_extension == '.json': df = pd.read_json(file_path) elif file_extension in ['.xlsx', '.xls']: df = pd.read_excel(file_path) elif file_extension == '.tsv': df = pd.read_csv(file_path, sep='\t') else: df = pd.read_csv(file_path) # Select numeric columns if columns: # Validate specified columns exist missing_cols = [col for col in columns if col not in df.columns] if missing_cols: return f"Error: Columns not found: {missing_cols}" numeric_df = df[columns].select_dtypes(include=['number']) else: numeric_df = df.select_dtypes(include=['number']) if numeric_df.empty: return "Error: No numeric columns found for correlation analysis" # Calculate correlation matrix correlation_matrix = numeric_df.corr() # Create the heatmap plt.figure(figsize=(10, 8)) sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, fmt='.2f', square=True, linewidths=0.5) plt.title('Correlation Heatmap') plt.tight_layout() # Save the plot plt.savefig(output_path, dpi=300, bbox_inches='tight') plt.close() result = { "heatmap_created": True, "columns_analyzed": list(correlation_matrix.columns), "total_correlations": len(correlation_matrix.columns) ** 2, "output_file": output_path, "file_size": Path(output_path).stat().st_size if Path(output_path).exists() else 0 } return json.dumps(result, indent=2) except Exception as e: return f"Error creating correlation heatmap: {str(e)}\n{traceback.format_exc()}" - src/visidata_mcp/server.py:687-689 (registration)The tool is registered as an MCP tool using the @mcp.tool() decorator on line 687, which exposes the create_correlation_heatmap function to the MCP server.
@mcp.tool() def create_correlation_heatmap(file_path: str, output_path: str, columns: Optional[List[str]] = None) -> str: