To improve the discoverability of tools for a model, focus on three main areas: clear documentation, standardized interfaces, and organized tool registries. Discoverability ensures the model can efficiently identify and use the right tools for specific tasks. This requires structuring tools in a way that aligns with how the model processes information and making their capabilities explicitly known through metadata and logical grouping.
First, provide detailed and consistent documentation for each tool. This includes descriptions of the tool’s purpose, input/output formats, and example usage scenarios. For instance, if a tool is designed to fetch weather data, document the exact parameters it accepts (e.g., latitude/longitude, date ranges) and the structure of the response (e.g., JSON with temperature, humidity). Use standardized naming conventions for tool functions and parameters—like get_weather(location, date)—to help the model recognize patterns. Additionally, include error-handling details (e.g., how the tool responds to invalid inputs) so the model can anticipate edge cases. Tools with ambiguous or incomplete documentation are harder for models to leverage correctly.
Second, implement a centralized tool registry or catalog. This acts as a searchable directory where tools are categorized by function, input type, or domain. For example, group tools under labels like “geospatial analysis,” “image processing,” or “APIs for financial data.” Each entry should include metadata such as a tool’s purpose, required permissions, and compatibility with other tools. To make this actionable, structure the registry in a machine-readable format like JSON or YAML, which the model can parse. For instance, a tool’s entry might include fields like name: "image_resizer", description: "Resizes images to specified dimensions", and tags: ["image", "preprocessing"]. This allows the model to filter tools based on task requirements, improving accuracy in selecting relevant options.
Finally, enable feedback loops to refine discoverability over time. Track how often tools are used, which combinations are selected together, and where the model struggles to find appropriate options. For example, if a data visualization tool is frequently overlooked, analyze whether its metadata lacks clarity or if it’s miscategorized. Use this data to update documentation, adjust tool groupings, or simplify complex interfaces. You can also implement user-driven tagging or ranking systems—such as allowing developers to rate a tool’s usability—to surface high-quality options. Regularly auditing and updating the tool ecosystem ensures it stays aligned with evolving model needs and real-world usage patterns.