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apply_formula

Apply an Excel formula to a specified cell with verification, ensuring accurate data manipulation in a workbook. Input filepath, sheet name, cell, and formula for precise execution.

Instructions

Apply Excel formula to cell. Excel formula will write to cell with verification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cellYes
filepathYes
formulaYes
sheet_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool registration and handler for 'apply_formula'. Validates input then delegates to core implementation.
    @mcp.tool()
    def apply_formula(
        filepath: str,
        sheet_name: str,
        cell: str,
        formula: str,
    ) -> str:
        """
        Apply Excel formula to cell.
        Excel formula will write to cell with verification.
        """
        try:
            full_path = get_excel_path(filepath)
            # First validate the formula
            validation = validate_formula_impl(full_path, sheet_name, cell, formula)
            if isinstance(validation, dict) and "error" in validation:
                return f"Error: {validation['error']}"
                
            # If valid, apply the formula
            from excel_mcp.calculations import apply_formula as apply_formula_impl
            result = apply_formula_impl(full_path, sheet_name, cell, formula)
            return result["message"]
        except (ValidationError, CalculationError) as e:
            return f"Error: {str(e)}"
        except Exception as e:
            logger.error(f"Error applying formula: {e}")
            raise
  • Core helper function that loads workbook, applies formula to cell, validates syntax, and saves the file.
    def apply_formula(
        filepath: str,
        sheet_name: str,
        cell: str,
        formula: str
    ) -> dict[str, Any]:
        """Apply any Excel formula to a cell."""
        try:
            if not validate_cell_reference(cell):
                raise ValidationError(f"Invalid cell reference: {cell}")
                
            wb = get_or_create_workbook(filepath)
            if sheet_name not in wb.sheetnames:
                raise ValidationError(f"Sheet '{sheet_name}' not found")
                
            sheet = wb[sheet_name]
            
            # Ensure formula starts with =
            if not formula.startswith('='):
                formula = f'={formula}'
                
            # Validate formula syntax
            is_valid, message = validate_formula(formula)
            if not is_valid:
                raise CalculationError(f"Invalid formula syntax: {message}")
                
            try:
                # Apply formula to the cell
                cell_obj = sheet[cell]
                cell_obj.value = formula
            except Exception as e:
                raise CalculationError(f"Failed to apply formula to cell: {str(e)}")
                
            try:
                wb.save(filepath)
            except Exception as e:
                raise CalculationError(f"Failed to save workbook after applying formula: {str(e)}")
            
            return {
                "message": f"Applied formula '{formula}' to cell {cell}",
                "cell": cell,
                "formula": formula
            }
            
        except (ValidationError, CalculationError) as e:
            logger.error(str(e))
            raise
        except Exception as e:
            logger.error(f"Failed to apply formula: {e}")
            raise CalculationError(str(e))
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'verification', which hints at error-checking, but doesn't explain what verification entails, potential side effects, permissions needed, or response behavior. This is inadequate for a mutation tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is brief with two sentences, front-loading the main action. It avoids unnecessary words, though it could be more structured (e.g., separating purpose from behavior).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (applying formulas with verification), no annotations, and 0% schema coverage, the description is incomplete. While an output schema exists, the description doesn't address key aspects like error handling, verification details, or how it differs from sibling tools, making it insufficient for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter details. The description doesn't add any meaning beyond the parameter names (e.g., what 'cell' format is expected, what 'verification' involves). It fails to compensate for the lack of schema documentation, leaving all 4 parameters poorly defined.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool applies an Excel formula to a cell with verification, which is a clear verb+resource combination. However, it doesn't distinguish this from sibling tools like 'write_data_to_excel' or 'validate_formula_syntax', making the purpose somewhat vague in context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'write_data_to_excel' or 'validate_formula_syntax'. The description lacks context about prerequisites, exclusions, or specific scenarios where this tool is preferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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