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To let Claude use an API from Node.js, define a tool the model can request, then have your application validate and execute that request. Claude returns structured input; it does not directly call functions inside your Node.js process. Your code sends the operation’s result back so Claude can respond to the user.
What Claude tool use means
Tool use is a request-and-response protocol. You describe a capability—such as looking up an order—and Claude may return a structured tool_use block asking your application to use it. Your Node.js code decides whether that request is allowed, runs the function or API call, and returns a tool_result. Claude can then use the result to form a reply.
The model proposes an operation; your application remains responsible for execution, permissions, and side effects. Making a tool available does not guarantee Claude will choose it. See Anthropic’s tool-use overview.
Set up the Node.js SDK
Install Anthropic’s official TypeScript SDK package:
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npm install @anthropic-ai/sdkThe current SDK documentation lists Node.js 20 LTS or later non-EOL releases as supported. Runtime requirements can change, so check the official TypeScript SDK documentation for the version you install.
Run the client on your server and supply the API key through the ANTHROPIC_API_KEY environment variable. Keep the key out of browser code: the SDK disables browser support by default to help prevent exposure of secret credentials. Choose a model identifier from the current API documentation rather than relying on an older code sample.
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Define a tool that matches your function
A custom tool definition gives Claude a stable name, an input schema, and a description of when the tool is appropriate. The description is optional but strongly recommended; explain the tool’s purpose and its boundaries. Keep the capability narrow and make the schema reflect the arguments your function actually accepts.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11For example, an order lookup tool might accept an order ID, but should not also expose unrelated account changes. A schema describes the expected structure of model-generated input; it does not grant permission to access an account or authorize a transaction. See the Messages API reference for the current tool-definition format.
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Run the tool-use loop
- Send the request. Call
client.messages.create()with a currently supported model, the tool definition, and the conversation messages. Anthropic’s SDK examples show the environment-based client setup. - Inspect the response. Check
stop_reasonand the returned content blocks. When a block has typetool_use, read itsname,id, andinput. A tool being available does not mean a tool call will be returned. - Dispatch only known tools. Map the requested name through an explicit allowlist in your application. Validate the generated input before making a network request or performing a write, then call the corresponding function. Do not treat a tool request as permission to perform a sensitive action.
- Return the result. Append the assistant response containing the tool request to the message history. Then send a new user message with a
tool_resultblock whosetool_use_idexactly matches the original block’sid. The result belongs immediately after the corresponding tool-use message; put tool-result blocks before any text in that result message. - Continue or stop. Claude can use the result to produce a user-facing answer, or request another tool. Repeat the cycle for further client-side calls. Handle ordinary completion, other stop reasons, and truncated output instead of assuming every response is a tool call.
The tool-use implementation guide describes the message ordering and result format. If an API operation fails, return an error result with the appropriate error indication so Claude can explain the failure; do not report fabricated success.
Validate inputs and protect side effects
Tool input is model-generated data. Before acting on it, apply ordinary application safeguards:
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- Check required fields, types, ranges, and allowed values in your code.
- Dispatch only tool names your application explicitly registers.
- Apply the signed-in user’s permissions before accessing data or performing side effects.
- Use timeouts and normal API error handling, and return failures in a form Claude can interpret.
- Keep API credentials on the server rather than embedding a private key in a browser application.
These checks are application responsibilities. A well-formed schema helps describe expected input, but it does not enforce user authorization.
Choose the right execution model
For a custom Node.js tool, your application executes the operation. Anthropic server-side tools are a distinct option: they do not share the custom-tool execution model, and their pricing can differ. Choose based on who must control execution and whether you need application-level authorization, retries, or other handling.
A manual loop is a clear way to learn and control the flow: inspect the request, execute it, and return the matching result. SDK helpers or tool runners may reduce boilerplate, but their availability and behavior can depend on SDK version; verify the current official documentation before adopting one. Streaming is another workflow consideration, but it does not change the core responsibility split: your application must execute custom tools.
Understand how tool use affects cost
Client-side custom tool use is charged like other Claude API requests. Server-side tools can incur additional usage-based charges. Tool descriptions and the tool-use and tool-result content also contribute tokens. Because model prices can change, consult Anthropic’s current pricing documentation for applicable rates rather than relying on dated figures.
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