analyze_excel
Perform statistical analysis on Excel data to calculate descriptive statistics, identify trends, and extract insights from numeric columns in spreadsheet files.
Instructions
Perform statistical analysis on Excel data.
Args:
file_path: Path to the Excel file
columns: Comma-separated list of columns to analyze (analyzes all numeric columns if None)
sheet_name: Name of the sheet to analyze (for Excel files)
Returns:
JSON string with statistical analysisInput Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | ||
| columns | No | ||
| sheet_name | No |
Implementation Reference
- mcp_excel_server/server.py:311-367 (handler)The handler function for the 'analyze_excel' tool. It reads the Excel file (or CSV/TSV/JSON), filters specified columns if provided, selects numeric columns, and computes descriptive statistics, correlation matrix, missing values count, and unique value counts per column. Returns JSON analysis or error.
@mcp.tool() def analyze_excel(file_path: str, columns: Optional[str] = None, sheet_name: Optional[str] = None) -> str: """ Perform statistical analysis on Excel data. Args: file_path: Path to the Excel file columns: Comma-separated list of columns to analyze (analyzes all numeric columns if None) sheet_name: Name of the sheet to analyze (for Excel files) Returns: JSON string with statistical analysis """ try: # Read file _, ext = os.path.splitext(file_path) ext = ext.lower() read_params = {} if ext in ['.xlsx', '.xls', '.xlsm'] and sheet_name is not None: read_params["sheet_name"] = sheet_name if ext in ['.xlsx', '.xls', '.xlsm']: df = pd.read_excel(file_path, **read_params) elif ext == '.csv': df = pd.read_csv(file_path) elif ext == '.tsv': df = pd.read_csv(file_path, sep='\t') elif ext == '.json': df = pd.read_json(file_path) else: return f"Unsupported file extension: {ext}" # Filter columns if specified if columns: column_list = [c.strip() for c in columns.split(',')] df = df[column_list] # Select only numeric columns for analysis numeric_df = df.select_dtypes(include=['number']) if numeric_df.empty: return json.dumps({"error": "No numeric columns found for analysis"}) # Perform analysis analysis = { "descriptive_stats": numeric_df.describe().to_dict(), "correlation": numeric_df.corr().to_dict(), "missing_values": numeric_df.isnull().sum().to_dict(), "unique_values": {col: int(numeric_df[col].nunique()) for col in numeric_df.columns} } return json.dumps(analysis, indent=2, default=str) except Exception as e: return json.dumps({"error": str(e)}) - mcp_excel_server/server.py:311-311 (registration)The @mcp.tool() decorator registers the analyze_excel function as an MCP tool.
@mcp.tool() - mcp_excel_server/server.py:653-662 (helper)A prompt template related to analyzing Excel data, which generates a user prompt for analysis tasks.
@mcp.prompt() def analyze_excel_data(file_path: str) -> str: """ Create a prompt for analyzing Excel data """ return f""" I have an Excel file at {file_path} that I'd like to analyze. Could you help me understand the data structure, perform basic statistical analysis, and identify any patterns or insights in the data? """