AI Tool Calling: Validate the Action Before Running It

Tool calling lets a model propose a structured operation, such as looking up a record or calculating a result. Your application decides whether and how to run it. Keep that distinction explicit: generated arguments are untrusted input until the application validates them against the user's permissions and task.

Why it matters

A syntactically valid request can still be inappropriate. A model may choose the wrong record, provide an impossible date or request an action the user never authorised.

A practical workflow

1. Define one tool around a clear operation, with required fields and narrow accepted values.
2. Validate arguments on the server and check resource ownership independently of anything the model says.
3. Separate read operations from writes, and require an appropriate review step before consequential external changes.
4. Return concise results and explicit errors so the model can stop or ask for missing information without guessing.

An example

A room-availability tool can accept a date and return open slots. A booking tool should separately verify the chosen slot, user identity and current availability. The assistant's earlier statement that a slot is free is not a guarantee it remains available.

A mistake to avoid

Avoid building a single unrestricted 'run any command' tool for a narrow app. Broad capabilities increase the number of mistakes your application must defend against and make the intended permissions harder to understand.

Try it and check the result

Send malformed dates, unknown identifiers and repeated requests through the tool layer directly. The application should reject or handle them safely without depending on the model to notice every problem first.

Reference and further reading

Anthropic: building effective agents.

The workflows and examples in this guide are illustrative. Product controls and availability can change; check the linked official guidance for your setup.

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