Function Tool Parameters: Comprehensive Examples For Docs

by Pedro Alvarez 58 views

Hey guys! Let's dive into making our documentation even better, especially when it comes to using @function_tool in our OpenAI integrations. We're talking about adding some seriously clear code examples for each parameter. This will help everyone understand how to configure their tools like pros. Think of it as leveling up our SDK's user-friendliness! In this article, we will be covering comprehensive examples for function tool parameters in documentation.

Why We Need Better Examples

Right now, our documentation is good, but it could be amazing. The goal is to provide specific, easy-to-understand examples for every parameter of the @function_tool decorator. This way, developers can see exactly how each parameter works and how to use it effectively. Let's break down the parameters we need to focus on.

Parameters We'll Cover

  1. name_override: How to explicitly set a tool's name.
  2. description_override: How to provide a custom description for a tool.
  3. docstring_style: How to use a specific docstring style to parse tool information.
  4. use_docstring_info: When and how to control whether docstring information is used.
  5. failure_error_function: How to use a custom function for handling tool errors.
  6. strict_mode: The behavior when strict mode is enabled or disabled.
  7. is_enabled: How to dynamically enable or disable a tool based on a condition.

Adding these examples will give developers a comprehensive guide for configuring their tools. It's all about making the SDK more accessible and user-friendly. Let's get into the specifics!

Diving Deep into Each Parameter

1. name_override: Setting a Custom Tool Name

When creating tools with the @function_tool decorator, sometimes you want to give your tool a name that's different from the function name. This is where name_override comes in super handy. With name_override, you can explicitly set the name of your tool, which can be especially useful for clarity or consistency in your API interactions. For example, imagine you have a function named fetch_customer_details, but you want the tool to be called simply getCustomerDetails in your API. Using name_override, you can achieve this effortlessly. This flexibility is key to making your tools more intuitive and aligned with your specific needs.

The name_override parameter allows developers to have full control over how their tools are presented to the outside world. Instead of relying on the function name, which might be more technical or internal-facing, you can craft a name that's user-friendly and self-explanatory. For instance, if you have a function called _calculate_discount_, you probably don’t want that showing up in your tool list. Instead, you could use name_override to set the tool name to calculateDiscount, making it much cleaner and easier to understand. This is particularly beneficial when dealing with complex systems or APIs where naming conventions are crucial for maintainability and ease of use. By providing a clear and concise name, you reduce the chances of confusion and make your tools more accessible to other developers or even non-technical users.

Furthermore, the explicit naming provided by name_override can also improve the discoverability of your tools. When users or systems are browsing through available tools, a well-chosen name can immediately convey the purpose and functionality of the tool. Think of it as giving your tool a clear and descriptive label, making it easier for others to find and use it. This is especially important in large applications with numerous tools, where effective organization and naming conventions are vital. By using name_override thoughtfully, you contribute to a more organized and user-friendly development environment, ultimately enhancing the overall efficiency and effectiveness of your projects.

2. description_override: Custom Tool Descriptions

Having a clear and concise description for your tools is crucial. It tells users exactly what the tool does without them having to dive into the code. The description_override parameter lets you provide a custom description for your tool, ensuring that it's easily understood by anyone interacting with it. Think of it as the tool's elevator pitch—a quick summary of its capabilities and purpose. This is super useful for tools that have complex functionalities or those that might not be immediately obvious from their names alone. For example, if you have a tool that calculates compound interest, you might want to provide a description like "Calculates the compound interest for a given principal amount, interest rate, and time period." This gives users a clear understanding of what the tool does and how to use it.

The ability to override the tool description is also incredibly useful for maintaining consistency across your API documentation. By providing custom descriptions, you can ensure that all your tools are described in a uniform style and level of detail. This not only enhances readability but also makes it easier for users to compare and select the right tool for their needs. Imagine trying to navigate a set of tools where some descriptions are detailed and others are vague—it would be frustrating and time-consuming. description_override helps you avoid this issue by giving you the power to define exactly how each tool is presented.

Moreover, a well-crafted description can significantly improve the usability of your tools in automated systems. When AI agents or other automated processes are selecting tools to use, they often rely on the tool descriptions to understand their capabilities. A clear and accurate description ensures that the right tool is chosen for the task, leading to more efficient and reliable outcomes. For instance, if you have two tools, one for summarizing text and another for translating text, the descriptions need to be precise to ensure the agent selects the correct tool based on the user's request. By using description_override to create informative descriptions, you empower your tools to be used effectively in both human-driven and automated workflows.

3. docstring_style: Parsing Tool Information

Docstrings are the unsung heroes of code documentation. They're the in-code explanations that help developers understand what a function does, what parameters it takes, and what it returns. The docstring_style parameter allows you to specify the style of docstring you're using, so the tool can correctly parse the information. Different docstring styles, like Google, NumPy, or reStructuredText, have different ways of formatting this information. By setting the docstring_style, you ensure that your tool extracts the necessary details accurately. This is crucial for automatically generating documentation and providing helpful information to users.

Using the docstring_style parameter effectively means that you can leverage existing documentation within your code to define your tools. Instead of having to manually specify the tool's parameters, descriptions, and return types, you can simply write them in your docstrings and let the tool parse them. This not only saves time but also helps to keep your documentation consistent and up-to-date with your code. For example, if you update a parameter in your function, you only need to update the docstring, and the tool will automatically reflect the change. This streamlined approach to documentation ensures that your tools are always well-documented and easy to use.

Furthermore, the docstring_style parameter plays a vital role in creating a professional and maintainable codebase. By adhering to a specific docstring style, you ensure that your documentation is consistent and easily understandable by others. This is particularly important in collaborative projects, where multiple developers may be working on the same code. A consistent docstring style makes it easier for everyone to read and understand the code, reducing the risk of errors and improving overall productivity. Additionally, many documentation generation tools rely on specific docstring styles to create API documentation automatically. By using docstring_style, you can seamlessly integrate your tools into automated documentation workflows, ensuring that your documentation is always comprehensive and up-to-date.

4. use_docstring_info: Controlling Docstring Usage

Sometimes, you might not want to use the information from your docstrings. That's where the use_docstring_info parameter comes in. This parameter gives you the power to control whether docstring information is used when creating your tool. It's like a switch that lets you toggle between using docstring details and ignoring them. This is particularly helpful when you want to provide specific information that differs from what's in your docstring, or when you simply don't have a docstring and prefer to define the tool's details manually. For example, you might have a detailed docstring for internal use but want to provide a simpler, more user-friendly description for the tool.

The ability to control docstring usage with use_docstring_info provides flexibility in how you manage your tool documentation. It allows you to mix and match information from different sources, ensuring that your tool is documented in the way that best suits your needs. For instance, you might use the docstring to define the tool's parameters and return types, but override the description with description_override to provide a more tailored explanation. This level of control is invaluable when you're dealing with complex tools or when you need to adapt your documentation to different audiences.

Moreover, use_docstring_info can be a lifesaver when you're refactoring code or working with legacy systems. In some cases, docstrings may be incomplete or outdated, and you might not have the time to update them immediately. By setting use_docstring_info to False, you can avoid relying on inaccurate information and instead provide the correct details through other parameters. This ensures that your tools are always documented correctly, even when the underlying code is in flux. It's a powerful tool for maintaining documentation quality and consistency, especially in dynamic development environments.

5. failure_error_function: Custom Error Handling

Errors happen, but how you handle them can make all the difference. The failure_error_function parameter lets you specify a custom function to handle errors that occur when your tool fails. This means you can provide user-friendly error messages or take specific actions when something goes wrong. Instead of just throwing a generic error, you can provide context and guidance to the user. For example, if a tool fails because a user doesn't have the necessary permissions, you can display a message explaining this and suggesting a solution. This makes your tools more robust and user-friendly.

By using a custom error function, you can also log errors for debugging purposes. This can be incredibly helpful for identifying and fixing issues in your code. Instead of having to sift through generic error logs, you can capture specific information about the error, such as the tool that failed, the parameters that were used, and the error message. This makes it much easier to diagnose and resolve problems, leading to a more stable and reliable system. Additionally, a well-designed error function can prevent sensitive information from being exposed in error messages, enhancing the security of your application.

Furthermore, failure_error_function allows you to tailor error messages to the specific needs of your users. Different users may require different levels of detail in error messages. For example, technical users may appreciate more verbose error messages that include technical details, while non-technical users may prefer simpler, more user-friendly messages. By using a custom error function, you can provide the appropriate level of detail for each user, enhancing their overall experience. This level of customization is crucial for creating tools that are both powerful and accessible.

Here's the code example we mentioned earlier:

from agents import function_tool, RunContextWrapper
from typing import Any

def my_custom_error_function(context: RunContextWrapper[Any], error: Exception) -> str:
    """A custom function to provide a user-friendly error message."""
    print(f"A tool call failed with the following error: {error}")
    return "An internal server error occurred. Please try again later."

@function_tool(failure_error_function=my_custom_error_function)
def get_user_profile(user_id: str) -> str:
    """Fetches a user profile from a mock API.
    
    This function demonstrates a "flaky" or failing API call.
    """
    if user_id == "user_123":
        return "User profile for user_123 successfully retrieved."
    else:
        raise ValueError(f"Could not retrieve profile for user_id: {user_id}. API returned an error.")

6. strict_mode: Enabling and Disabling Strict Mode

strict_mode is like a safety net for your tools. When enabled, it enforces strict type checking and validation of parameters. This means that if the parameters passed to your tool don't match the expected types, an error will be raised. This helps prevent unexpected behavior and ensures that your tools are used correctly. Disabling strict mode, on the other hand, allows for more flexibility but also increases the risk of errors. Understanding when to use strict mode is crucial for building robust and reliable tools.

Enabling strict mode is particularly useful during development and testing. It helps to catch errors early on, before they can cause problems in production. By enforcing type checking, strict mode ensures that your tools are used as intended and that the data they receive is valid. This can save you a lot of time and effort in debugging, as you'll be alerted to potential issues immediately. It also promotes good coding practices by encouraging you to define clear parameter types and validate inputs.

However, there are situations where disabling strict mode might be necessary. For example, if you're working with legacy code or integrating with systems that have flexible input requirements, you might need to disable strict mode to avoid compatibility issues. In these cases, it's important to carefully consider the trade-offs and implement other forms of validation to ensure the integrity of your data. The key is to use strict_mode strategically, balancing the need for safety with the need for flexibility.

7. is_enabled: Dynamic Tool Control

Sometimes, you need to enable or disable tools dynamically based on certain conditions. The is_enabled parameter allows you to do just that. It's like a master switch that can turn a tool on or off based on a condition you define. This is incredibly useful for controlling access to tools, implementing feature flags, or managing resources. For example, you might want to disable a tool if a user doesn't have the necessary permissions, or if a certain feature is not yet available. is_enabled gives you the flexibility to manage your tools in a dynamic and responsive way.

Using is_enabled can also help you optimize resource usage. For example, if you have a tool that consumes a lot of resources, you might want to disable it when it's not needed. This can help to reduce costs and improve the performance of your system. Similarly, you can use is_enabled to implement A/B testing, where you enable a tool for a subset of users to test its effectiveness before rolling it out to everyone. This allows you to gather data and make informed decisions about which tools to enable and when.

Moreover, the dynamic control provided by is_enabled can enhance the security of your application. By disabling tools that are not needed, you reduce the attack surface and make it harder for attackers to exploit vulnerabilities. For instance, you might disable a tool that's used for administrative tasks when it's not being used, preventing unauthorized access. This proactive approach to security can significantly improve the overall resilience of your system.

Conclusion: Leveling Up Our Documentation

Adding these examples will provide developers with a comprehensive and easy-to-understand guide for configuring their tools. It's all about making the SDK more accessible and user-friendly. By diving deep into each parameter of @function_tool, we're empowering our users to build better, more robust tools. So, let's get these examples added and make our documentation shine! Remember, clear documentation leads to happy developers and successful projects. Let's make it happen, guys! This detailed approach will not only enhance the user experience but also promote better coding practices and a deeper understanding of the OpenAI SDK. By providing real-world examples and clear explanations, we can empower developers to create more sophisticated and effective applications. The future of AI tool development is bright, and with comprehensive documentation, we can ensure that everyone has the resources they need to succeed.