# tool_choice and Parallel Calls — AI Agents and Tool Use

Source: https://www.geekswithgeeks.com/en/ai-agents-mcp/use-choice-parallel

> Control whether tools are used and handle several tool calls returned in one reply.

## Steering and batching

In the Claude API, `tool_choice` controls tool use: `auto` lets the model decide, `any` forces it to use some tool, `tool` forces a specific tool, and `none` disables tools. The model may also return **several** `tool_use` blocks in one reply when the calls are independent; you run them all and send back one `tool_result` per block, matched by id, in a single user message.

## Returning parallel results

Each result keeps the id of its request, so order does not matter. This ran as shown.

```python
calls = [("c1", "get_balance", {"account_id": 1}),
         ("c2", "get_balance", {"account_id": 2})]
results = [{"tool_use_id": cid, "content": call_tool(n, a)["content"]}
           for cid, n, a in calls]
print(results)
```

Output:

```
[{'tool_use_id': 'c1', 'content': 'account 1: 1500'}, {'tool_use_id': 'c2', 'content': 'account 2: 1500'}]
```

## Run independent calls concurrently

If three tool calls do not depend on each other, run them with threads or `asyncio` to cut wait time. Calls that change shared state may need to run in order.

**Quiz:** How are parallel tool results matched to requests?

- [x] By tool_use id
- [ ] By the order they finish
- [ ] By alphabetical name
- [ ] By file size

*Answer:* By tool_use id. Each tool_result carries the tool_use_id of the call it answers.