Excel MCP Server

Instructions

Create a pivot table from Excel data.

Args:
    file_path: Path to the Excel file
    index: Column to use as the pivot table index
    columns: Optional column to use as the pivot table columns
    values: Column to use as the pivot table values
    aggfunc: Aggregation function ('mean', 'sum', 'count', etc.)
    sheet_name: Name of the sheet to pivot (for Excel files)
    
Returns:
    Pivot table as string

Implementation Reference

• mcp_excel_server/server.py:412-477 (handler)

  The main handler function for the 'pivot_table' tool. Decorated with @mcp.tool() which registers it as an MCP tool. Reads the Excel/CSV file, prepares pivot table parameters based on inputs, maps aggregation function strings to numpy/pandas functions, creates the pivot table using pd.pivot_table, and returns it as a formatted string.

  @mcp.tool()
  def pivot_table(file_path: str, index: str, columns: Optional[str] = None, 
                values: str = None, aggfunc: str = "mean", 
                sheet_name: Optional[str] = None) -> str:
      """
      Create a pivot table from Excel data.
      
      Args:
          file_path: Path to the Excel file
          index: Column to use as the pivot table index
          columns: Optional column to use as the pivot table columns
          values: Column to use as the pivot table values
          aggfunc: Aggregation function ('mean', 'sum', 'count', etc.)
          sheet_name: Name of the sheet to pivot (for Excel files)
          
      Returns:
          Pivot table as string
      """
      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}"
          
          # Configure pivot table params
          pivot_params = {"index": index}
          if columns:
              pivot_params["columns"] = columns
          if values:
              pivot_params["values"] = values
              
          # Map string aggfunc to actual function
          if aggfunc == "mean":
              pivot_params["aggfunc"] = np.mean
          elif aggfunc == "sum":
              pivot_params["aggfunc"] = np.sum
          elif aggfunc == "count":
              pivot_params["aggfunc"] = len
          elif aggfunc == "min":
              pivot_params["aggfunc"] = np.min
          elif aggfunc == "max":
              pivot_params["aggfunc"] = np.max
          else:
              return f"Unsupported aggregation function: {aggfunc}"
          
          # Create pivot table
          pivot = pd.pivot_table(df, **pivot_params)
          
          return pivot.to_string()
      except Exception as e:
          return f"Error creating pivot table: {str(e)}"

• mcp_excel_server/server.py:412-412 (registration)

  The @mcp.tool() decorator on the pivot_table function serves as the registration mechanism in FastMCP, automatically registering the function as a tool named 'pivot_table' based on its definition.

  @mcp.tool()

• mcp_excel_server/server.py:413-429 (schema)

  The function signature and docstring define the input schema (parameters with types and descriptions) and output schema for the pivot_table tool.

  def pivot_table(file_path: str, index: str, columns: Optional[str] = None, 
                values: str = None, aggfunc: str = "mean", 
                sheet_name: Optional[str] = None) -> str:
      """
      Create a pivot table from Excel data.
      
      Args:
          file_path: Path to the Excel file
          index: Column to use as the pivot table index
          columns: Optional column to use as the pivot table columns
          values: Column to use as the pivot table values
          aggfunc: Aggregation function ('mean', 'sum', 'count', etc.)
          sheet_name: Name of the sheet to pivot (for Excel files)
          
      Returns:
          Pivot table as string
      """