Claude Opus 4.6 supports tool calling through the Claude API feature set, which lets your application define tools (functions) with a schema and allows the model to request tool invocations as part of solving a task. In agent-style systems, the model becomes a planner and coordinator: it decides what information it needs, calls tools to fetch or compute it, and then produces a final response using the tool results.
To build reliable agents, keep tools small, typed, and safe. Define strict JSON schemas for tool inputs, validate all arguments, cap tool outputs, and log every tool call. Put an approval layer in front of risky actions: the model can propose a command, but your system decides whether it’s allowed. Also enforce iteration limits to prevent loops, and require the agent to produce a plan and checkpoints so you can monitor progress.
Retrieval tools are usually the most valuable “agent capability” in production. Implement a search_docs tool that queries Milvus or managed Zilliz Cloud with metadata filters, returns chunk IDs and text, and then instruct Opus 4.6 to answer only from those chunks. This approach yields agents that feel grounded and predictable, especially for developer documentation assistants and internal knowledge bots.