prompt_from_file_to_file

Send prompts from a file to multiple LLM models and store their responses in specified directories, using absolute paths for file input and output.

Instructions

Send a prompt from a file to multiple LLM models and save responses to files. IMPORTANT: You MUST provide absolute paths (e.g., /path/to/file or C:\path\to\file) for both file and output directory, not relative paths.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`abs_file_path`	Yes	Absolute path to the file containing the prompt (must be an absolute path, not relative)
`abs_output_dir`	No	Absolute directory path to save the response files to (must be an absolute path, not relative. Default: current directory)	.
`models_prefixed_by_provider`	No	List of models with provider prefixes (e.g., 'openai:gpt-4o' or 'o:gpt-4o'). If not provided, uses default models.

Implementation Reference

src/just_prompt/molecules/prompt_from_file_to_file.py:15-69 (handler)

Core handler function: reads prompt from input file, prompts multiple models using prompt_from_file helper, saves each response to a markdown file in the output directory named after input stem and model.

def prompt_from_file_to_file(
    abs_file_path: str, models_prefixed_by_provider: List[str] = None, abs_output_dir: str = "."
) -> List[str]:
    """
    Read text from a file, send it as prompt to multiple models, and save responses to files.

    Args:
        abs_file_path: Absolute path to the text file (must be an absolute path, not relative)
        models_prefixed_by_provider: List of model strings in format "provider:model"
                                    If None, uses the DEFAULT_MODELS environment variable
        abs_output_dir: Absolute directory path to save response files (must be an absolute path, not relative)

    Returns:
        List of paths to the output files
    """
    # Validate output directory
    output_path = Path(abs_output_dir)
    if not output_path.exists():
        output_path.mkdir(parents=True, exist_ok=True)

    if not output_path.is_dir():
        raise ValueError(f"Not a directory: {abs_output_dir}")

    # Get the base name of the input file
    input_file_name = Path(abs_file_path).stem

    # Get responses
    responses = prompt_from_file(abs_file_path, models_prefixed_by_provider)

    # Save responses to files
    output_files = []

    # Get the models that were actually used
    models_used = models_prefixed_by_provider
    if not models_used:
        default_models = os.environ.get("DEFAULT_MODELS", DEFAULT_MODEL)
        models_used = [model.strip() for model in default_models.split(",")]

    for i, (model_string, response) in enumerate(zip(models_used, responses)):
        # Sanitize model string for filename (replace colons with underscores)
        safe_model_name = model_string.replace(":", "_")

        # Create output filename with .md extension
        output_file = output_path / f"{input_file_name}_{safe_model_name}.md"

        # Write response to file as markdown
        try:
            with open(output_file, "w", encoding="utf-8") as f:
                f.write(response)
            output_files.append(str(output_file))
        except Exception as e:
            logger.error(f"Error writing response to {output_file}: {e}")
            output_files.append(f"Error: {str(e)}")

    return output_files

src/just_prompt/server.py:59-69 (schema)

Pydantic input schema for validating tool arguments: input file path, optional models list, and output directory.

class PromptFromFileToFileSchema(BaseModel):
    abs_file_path: str = Field(..., description="Absolute path to the file containing the prompt (must be an absolute path, not relative)")
    models_prefixed_by_provider: Optional[List[str]] = Field(
        None, 
        description="List of models with provider prefixes (e.g., 'openai:gpt-4o' or 'o:gpt-4o'). If not provided, uses default models."
    )
    abs_output_dir: str = Field(
        default=".", 
        description="Absolute directory path to save the response files to (must be an absolute path, not relative. Default: current directory)"
    )

src/just_prompt/server.py:132-136 (registration)

Tool registration in the MCP server's list_tools() method, specifying name, description, and input schema.

Tool(
    name=JustPromptTools.PROMPT_FROM_FILE_TO_FILE,
    description="Send a prompt from a file to multiple LLM models and save responses to files. IMPORTANT: You MUST provide absolute paths (e.g., /path/to/file or C:\\path\\to\\file) for both file and output directory, not relative paths.",
    inputSchema=PromptFromFileToFileSchema.schema(),
),

src/just_prompt/server.py:186-197 (helper)

Server-side dispatch in call_tool(): extracts arguments, calls the handler function, formats output file paths as response.

elif name == JustPromptTools.PROMPT_FROM_FILE_TO_FILE:
    output_dir = arguments.get("abs_output_dir", ".")
    models_to_use = arguments.get("models_prefixed_by_provider")
    file_paths = prompt_from_file_to_file(
        arguments["abs_file_path"], 
        models_to_use,
        output_dir
    )
    return [TextContent(
        type="text",
        text=f"Responses saved to:\n" + "\n".join(file_paths)
    )]

src/just_prompt/server.py:39-39 (registration)
Tool name constant defined in JustPromptTools class.
```
PROMPT_FROM_FILE_TO_FILE = "prompt_from_file_to_file"
```

Just Prompt