Defining safe and structured tool interfaces for large language models (LLMs) involves creating clear protocols for how the model interacts with external tools while minimizing risks. Start by designing interfaces with strict input validation and output formatting. For example, if an LLM needs to call a weather API, define the exact parameters (like location and temperature unit) and ensure the model can only pass valid values. Use schema validation tools like JSON Schema to enforce data types, ranges, and required fields. This prevents malformed requests and reduces errors in downstream systems. Additionally, include error-handling rules, such as fallback responses when a tool fails, to maintain reliability.
Structure interfaces by standardizing how tools are described and invoked. A common approach is to define tools using a declarative format, such as OpenAPI specifications or custom JSON schemas, which outline inputs, outputs, and allowed operations. For instance, a calendar scheduling tool might require a start time, end time, and time zone, with outputs confirming the event ID or failure reason. Use enumerated values for fields like time zones to limit ambiguity. Provide explicit documentation for each tool’s purpose, parameters, and edge cases so developers and the LLM can use it correctly. Structured outputs (e.g., JSON with predefined keys) ensure the model parses results consistently, avoiding misinterpretation of free-text responses.
Prioritize security by isolating sensitive operations and enforcing access controls. For example, if a tool handles user data, require authentication tokens or API keys, and ensure the LLM cannot access these credentials directly. Implement rate limits to prevent abuse and audit logs to track tool usage. Versioning interfaces is also critical—maintain backward compatibility when updating tools to avoid breaking existing integrations. Test interfaces rigorously with adversarial inputs (e.g., invalid formats or extreme values) to identify vulnerabilities. By combining strict validation, clear documentation, and security measures, developers can create robust interfaces that enable LLMs to interact safely with external systems while reducing unintended behavior.