ipybox

# Saving Code Actions as Reusable Tools Save executed Python code as a tool for later reuse. ## Package Structure ``` gentools/<category>/<tool>/ ├── __init__.py # Empty file ├── api.py # Public interface with structured models └── impl.py # Implementation details ``` ## Procedure ### 1. Create Package Directory ```bash mkdir -p gentools/<category>/<tool> ``` Create empty `__init__.py` files in both `<category>` and `<tool>` directories. ### 2. Define Tool API (`api.py`) ```python from __future__ import annotations from pydantic import BaseModel, Field class OutputModel(BaseModel): """Description of output.""" field: type = Field(..., title="Description") def run(param1: type, param2: type = default) -> OutputModel: """Tool description. Args: param1: Description param2: Description (default: value) Returns: OutputModel with structured data """ from .impl import implementation_function return implementation_function(param1, param2) ``` Requirements: - Define Pydantic models for structured output - Create `run()` function with typed parameters - Use lazy import from `impl.py` inside `run()` - Include comprehensive docstring - Export `OutputModel` and `run` in `gentools/<category>/<tool>/__init__.py`: ```python from .api import OutputModel, run __all__ = ["OutputModel", "run"] ``` ### 3. Implement Details (`impl.py`) ```python from __future__ import annotations from mcptools.<category>.<tool> import Params, run_parsed from .api import OutputModel def implementation_function(param1: type, param2: type) -> OutputModel: """Implementation description.""" # Use tools from mcptools or gentools packages result = run_parsed(Params(...)) # Transform and return structured output return OutputModel(field=result.data) ``` Requirements: - Import tools from `mcptools` or `gentools` packages - Import models from `api.py` - Return structured models defined in `api.py` ### 4. Test the Tool ```python from gentools.<category>.<tool>.api import run result = run(param1=value1, param2=value2) print(result) ``` ## Best Practices - **Separation**: Keep API clean; hide complexity in implementation - **Type Safety**: Use Pydantic models for all outputs - **Modularity**: Break complex logic into smaller functions - **Defaults**: Provide sensible defaults for optional parameters