preview_data
Preview and inspect data structure before plotting to verify loading and understand column types, statistics, and missing values.
Instructions
Preview and inspect data before creating plots.
Returns a comprehensive summary including:
Dataset shape (rows and columns)
Column names and data types
First few rows of data
Basic statistics for numeric columns
Missing value counts
This helps verify data loaded correctly and understand its structure.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data_source | Yes | Data source configuration (file, URL, or inline data) | |
| rows | No | Number of rows to preview (default: 5) |
Implementation Reference
- src/plotnine_mcp/server.py:723-787 (handler)The main handler function for the 'preview_data' tool. Loads data from the provided data_source, generates a comprehensive preview including dataset shape, column types, sample rows, numeric statistics, and missing value summary, then returns it as TextContent.
async def preview_data_handler(arguments: dict[str, Any]) -> list[TextContent]: """Handle preview_data tool calls.""" try: # Parse data source data_source = DataSource(**arguments["data_source"]) rows = arguments.get("rows", 5) # Load data try: data = load_data(data_source) except DataLoadError as e: return [ TextContent( type="text", text=f"Data loading error: {str(e)}\n\nPlease check:\n- File path or URL is correct\n- File format is supported\n- Data is properly formatted", ) ] # Build preview message message = "Data Preview\n" + "=" * 50 + "\n\n" # Dataset shape message += f"Shape: {data.shape[0]} rows × {data.shape[1]} columns\n\n" # Column information message += "Columns:\n" for col in data.columns: dtype = str(data[col].dtype) message += f" - {col} ({dtype})\n" message += "\n" # First N rows message += f"First {min(rows, len(data))} rows:\n" message += "-" * 50 + "\n" preview_df = data.head(rows) message += preview_df.to_string(index=False) + "\n\n" # Basic statistics for numeric columns numeric_cols = data.select_dtypes(include=['number']).columns if len(numeric_cols) > 0: message += "Numeric Column Statistics:\n" message += "-" * 50 + "\n" stats_df = data[numeric_cols].describe() message += stats_df.to_string() + "\n\n" # Missing values missing = data.isnull().sum() if missing.sum() > 0: message += "Missing Values:\n" message += "-" * 50 + "\n" for col, count in missing[missing > 0].items(): pct = (count / len(data)) * 100 message += f" - {col}: {count} ({pct:.1f}%)\n" else: message += "No missing values found.\n" return [TextContent(type="text", text=message)] except Exception as e: return [ TextContent( type="text", text=f"Error previewing data: {str(e)}\n\nPlease check your data source configuration.", ) ] - src/plotnine_mcp/server.py:301-337 (schema)Input schema definition for the 'preview_data' tool, specifying data_source (file/url/inline with format) and optional rows parameter.
inputSchema={ "type": "object", "properties": { "data_source": { "type": "object", "description": "Data source configuration (file, URL, or inline data)", "properties": { "type": { "type": "string", "enum": ["file", "url", "inline"], "description": "Source type", }, "path": { "type": "string", "description": "File path or URL (for file/url types)", }, "data": { "type": "array", "items": {"type": "object"}, "description": "Inline data as array of objects (for inline type)", }, "format": { "type": "string", "enum": ["csv", "json", "parquet", "excel"], "description": "Data format (auto-detected if not specified)", }, }, "required": ["type"], }, "rows": { "type": "integer", "default": 5, "description": "Number of rows to preview (default: 5)", }, }, "required": ["data_source"], }, - src/plotnine_mcp/server.py:289-338 (registration)Registration of the 'preview_data' tool in the list_tools() function, including name, description, and schema.
Tool( name="preview_data", description="""Preview and inspect data before creating plots. Returns a comprehensive summary including: - Dataset shape (rows and columns) - Column names and data types - First few rows of data - Basic statistics for numeric columns - Missing value counts This helps verify data loaded correctly and understand its structure.""", inputSchema={ "type": "object", "properties": { "data_source": { "type": "object", "description": "Data source configuration (file, URL, or inline data)", "properties": { "type": { "type": "string", "enum": ["file", "url", "inline"], "description": "Source type", }, "path": { "type": "string", "description": "File path or URL (for file/url types)", }, "data": { "type": "array", "items": {"type": "object"}, "description": "Inline data as array of objects (for inline type)", }, "format": { "type": "string", "enum": ["csv", "json", "parquet", "excel"], "description": "Data format (auto-detected if not specified)", }, }, "required": ["type"], }, "rows": { "type": "integer", "default": 5, "description": "Number of rows to preview (default: 5)", }, }, "required": ["data_source"], }, ), - src/plotnine_mcp/server.py:533-534 (registration)Dispatch/registration logic in the call_tool handler that routes 'preview_data' calls to the preview_data_handler function.
elif name == "preview_data": return await preview_data_handler(arguments)