One of the most powerful features of the Claude API is tool use — the ability for Claude to call external functions during a conversation. This turns Claude from a text generator into an agent that can take actions, retrieve data, and interact with real systems.
In this article, we walk through how tool use works, when to use it, and how to build practical agents with the Anthropic SDK.
How Tool Use Works
When you send a message to Claude with a list of available tools, Claude can decide to call one or more of those tools instead of (or in addition to) generating text. The flow works like this:
- You define tools with names, descriptions, and input schemas
- You send a message to Claude along with the tool definitions
- Claude analyzes the request and decides which tools to call
- You receive tool call requests, execute them, and send the results back
- Claude uses the results to formulate a final response
The key insight is that Claude does not execute the tools itself. It generates structured requests, and your application handles the actual execution. This gives you full control over what actions are taken.
Defining Tools
Tools are defined using JSON Schema. Each tool needs a name, a description, and an input schema that specifies the parameters. The description is crucial because Claude uses it to decide when and how to use the tool.
A well-designed tool has a clear, specific description that explains not just what it does, but when it should be used. Vague descriptions lead to unreliable tool selection.
When to Use Tool Use
Tool use is the right choice when you need Claude to:
- Retrieve real-time data — weather, stock prices, database queries
- Take actions — send emails, create tickets, update records
- Access external systems — APIs, databases, file systems
- Perform calculations — mathematical operations, data transformations
- Chain multiple steps — workflows that require sequential operations
It is not the right choice for tasks that Claude can handle with its built-in knowledge, or when the overhead of tool calls is not justified.
Building a Practical Agent
Let us build an agent that can manage a project task board. It needs three tools: one to list tasks, one to create a task, and one to update a task status.
The agent loop works as follows: send the user message with tool definitions, check if Claude wants to use any tools, execute those tools, send the results back, and repeat until Claude generates a final text response.
This pattern — the agentic loop — is the foundation of all tool-use agents. The loop continues as long as Claude keeps requesting tool calls, and stops when it has enough information to respond.
Handling Multiple Tool Calls
Claude can request multiple tool calls in a single response. This is useful for parallel operations — for example, fetching data from two different sources simultaneously. Your application should handle all tool calls and return all results before sending them back to Claude.
Error Handling
When a tool call fails, you should return the error message as the tool result rather than throwing an exception. This lets Claude understand what went wrong and potentially try a different approach or inform the user about the issue.
Robust error handling is what separates a demo from a production agent. Always validate inputs, handle timeouts, and return meaningful error messages.
Streaming with Tool Use
For production applications, you will want to use streaming to provide real-time feedback. The Anthropic SDK supports streaming with tool use — you receive events as Claude generates its response, including tool call events that you can process immediately.
This is particularly important for agents that perform long-running operations. Streaming lets you show progress to the user while tools are being executed.
Security Considerations
Tool use introduces real security concerns because your agent can take actions in the real world. Key practices:
- Validate all inputs before executing tool calls
- Implement rate limiting to prevent abuse
- Use least-privilege access for tool permissions
- Log all tool executions for audit trails
- Add human-in-the-loop for destructive or irreversible actions
Never trust tool inputs without validation. Claude generates the inputs based on user messages, and user messages can be crafted to exploit vulnerabilities.
Advanced Patterns
Tool chaining: Design tools that work together. A "search" tool finds entities, and an "update" tool modifies them. Claude will naturally chain these in the right order.
Dynamic tool sets: You do not have to send all tools with every request. Include only the tools relevant to the current conversation context to improve reliability and reduce latency.
Tool results as context: The results of tool calls become part of the conversation context. Design your tool responses to include relevant information that helps Claude make better decisions in subsequent turns.
Conclusion
Tool use transforms Claude from a language model into an agent platform. By defining clear tools, implementing robust error handling, and following security best practices, you can build agents that reliably interact with real-world systems.
The Anthropic SDK makes this straightforward to implement, and the agentic loop pattern provides a solid foundation for any tool-use application. Start with simple tools and build up complexity as you validate each layer.