Tool use (or function calling) allows LLMs to interact with external systems by generating structured API calls. Instead of just generating text, models like GPT-5 and Claude 4 can search databases, call APIs, execute code, and perform real-world actions.
Common Tool Types
- Search: Web search, database queries, RAG retrieval
- Code execution: Python interpreters, sandboxed environments
- APIs: Weather, calendars, CRMs, payment systems
- File operations: Read, write, analyze documents
Tool Use Risks
- Hallucinated parameters causing incorrect actions
- Prompt injection triggering unauthorized tool calls
- Excessive API calls increasing costs
- Data exfiltration through tool responses
Monitoring Tool Invocations
Track every tool call for safety:
- Log all function names and parameters
- Validate parameters before execution
- Rate limit sensitive operations
- Alert on unusual tool patterns
Why monitor AI tool use?
Tool use amplifies AI risks. A hallucinated function call could delete data, send incorrect emails, or make unauthorized purchases. Monitor tool invocations to catch errors before they cause damage.
Practical example
A model proposes sending a message, and the application checks the user’s authorization and required confirmation.
What to check
Validate arguments, access, side effects, duplicate execution, tool errors, and final application state.
Limits and tradeoffs
A tool proposal is not evidence that an action happened or that the action was authorized.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.