get_data_sample
Extract a data sample from files to preview content and structure. Specify file path and row count to retrieve JSON-formatted sample data for analysis.
Instructions
Get a sample of data from a file.
Args: file_path: Path to the data file rows: Number of rows to return (default: 10)
Returns: Sample data in JSON format
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | ||
| rows | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/visidata_mcp/server.py:121-181 (handler)Main implementation of get_data_sample tool that loads data using pandas and returns a sample of N rows in JSON format. Handles multiple file types (CSV, JSON, Excel, TSV) and properly converts numpy/pandas types for JSON serialization.
@mcp.tool() def get_data_sample(file_path: str, rows: int = 10) -> str: """ Get a sample of data from a file. Args: file_path: Path to the data file rows: Number of rows to return (default: 10) Returns: Sample data in JSON format """ try: import pandas as pd from pathlib import Path file_extension = Path(file_path).suffix.lower() # Load with pandas 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) # Get sample rows sample_df = df.head(rows) # Convert to records for JSON serialization sample_data = [] for _, row in sample_df.iterrows(): row_data = {} for col in df.columns: value = row[col] # Handle pandas/numpy types for JSON serialization if pd.isna(value): row_data[col] = None elif hasattr(value, 'item'): # numpy types row_data[col] = value.item() else: row_data[col] = str(value) if value is not None else None sample_data.append(row_data) result = { "filename": Path(file_path).name, "total_rows": len(df), "total_columns": len(df.columns), "sample_rows": len(sample_data), "columns": list(df.columns), "data": sample_data } return json.dumps(result, indent=2) except Exception as e: return f"Error getting data sample: {str(e)}\n{traceback.format_exc()}" - src/visidata_mcp/server.py:121-121 (registration)Tool registration via the @mcp.tool() decorator, which registers get_data_sample as an MCP tool in the FastMCP server instance.
@mcp.tool() - src/visidata_mcp/server.py:122-132 (schema)Function signature and docstring defining the input/output schema: takes file_path (str) and optional rows (int=10), returns JSON string with sample data including filename, row/column counts, and actual data rows.
def get_data_sample(file_path: str, rows: int = 10) -> str: """ Get a sample of data from a file. Args: file_path: Path to the data file rows: Number of rows to return (default: 10) Returns: Sample data in JSON format """