claude_tool_use

Tool use with Claude Claude is capable of interacting with external client-side tools and functions, allowing you to equip Claude with your own custom tools to perform a wider variety of tasks.

Learn everything you need to master tool use with Claude via our new comprehensive tool use course! Please continue to share your ideas and suggestions using this form.

Here’s an example of how to provide tools to Claude using the Messages API:

Shell

Python

import anthropic

client = anthropic.Anthropic()

response = client.messages.create( model="claude-3-7-sonnet-20250219", max_tokens=1024, tools=[ { "name": "get_weather", "description": "Get the current weather in a given location", "input_schema": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", } }, "required": ["location"], }, } ], messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}], ) print(response) ​ How tool use works Integrate external tools with Claude in these steps:

1 Provide Claude with tools and a user prompt

Define tools with names, descriptions, and input schemas in your API request. Include a user prompt that might require these tools, e.g., “What’s the weather in San Francisco?” 2 Claude decides to use a tool

Claude assesses if any tools can help with the user’s query. If yes, Claude constructs a properly formatted tool use request. The API response has a stop_reason of tool_use, signaling Claude’s intent. 3 Extract tool input, run code, and return results

On your end, extract the tool name and input from Claude’s request. Execute the actual tool code client-side. Continue the conversation with a new user message containing a tool_result content block. 4 Claude uses tool result to formulate a response

Claude analyzes the tool results to craft its final response to the original user prompt. Note: Steps 3 and 4 are optional. For some workflows, Claude’s tool use request (step 2) might be all you need, without sending results back to Claude.

Tools are user-provided

It’s important to note that Claude does not have access to any built-in server-side tools. All tools must be explicitly provided by you, the user, in each API request. This gives you full control and flexibility over the tools Claude can use.

The computer use (beta) functionality is an exception - it introduces tools that are provided by Anthropic but implemented by you, the user.

​ How to implement tool use ​ Choosing a model Generally, use Claude 3.7 Sonnet, Claude 3.5 Sonnet or Claude 3 Opus for complex tools and ambiguous queries; they handle multiple tools better and seek clarification when needed.

Use Claude 3.5 Haiku or Claude 3 Haiku for straightforward tools, but note they may infer missing parameters.

If using Claude 3.7 Sonnet with tool use and extended thinking, refer to our guide here for more information. ​ Specifying tools Tools are specified in the tools top-level parameter of the API request. Each tool definition includes:

Parameter Description name The name of the tool. Must match the regex ^[a-zA-Z0-9_-]{1,64}$. description A detailed plaintext description of what the tool does, when it should be used, and how it behaves. input_schema A JSON Schema object defining the expected parameters for the tool.

Example simple tool definition

​ Tool use system prompt When you call the Anthropic API with the tools parameter, we construct a special system prompt from the tool definitions, tool configuration, and any user-specified system prompt. The constructed prompt is designed to instruct the model to use the specified tool(s) and provide the necessary context for the tool to operate properly:

In this environment you have access to a set of tools you can use to answer the user's question. {{ FORMATTING INSTRUCTIONS }} String and scalar parameters should be specified as is, while lists and objects should use JSON format. Note that spaces for string values are not stripped. The output is not expected to be valid XML and is parsed with regular expressions. Here are the functions available in JSONSchema format: {{ TOOL DEFINITIONS IN JSON SCHEMA }} {{ USER SYSTEM PROMPT }} {{ TOOL CONFIGURATION }} ​ Best practices for tool definitions To get the best performance out of Claude when using tools, follow these guidelines:

Provide extremely detailed descriptions. This is by far the most important factor in tool performance. Your descriptions should explain every detail about the tool, including: What the tool does When it should be used (and when it shouldn’t) What each parameter means and how it affects the tool’s behavior Any important caveats or limitations, such as what information the tool does not return if the tool name is unclear. The more context you can give Claude about your tools, the better it will be at deciding when and how to use them. Aim for at least 3-4 sentences per tool description, more if the tool is complex. Prioritize descriptions over examples. While you can include examples of how to use a tool in its description or in the accompanying prompt, this is less important than having a clear and comprehensive explanation of the tool’s purpose and parameters. Only add examples after you’ve fully fleshed out the description.

Example of a good tool description

JSON

{ "name": "get_stock_price", "description": "Retrieves the current stock price for a given ticker symbol. The ticker symbol must be a valid symbol for a publicly traded company on a major US stock exchange like NYSE or NASDAQ. The tool will return the latest trade price in USD. It should be used when the user asks about the current or most recent price of a specific stock. It will not provide any other information about the stock or company.", "input_schema": { "type": "object", "properties": { "ticker": { "type": "string", "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." } }, "required": ["ticker"] } }

Example poor tool description

JSON

{ "name": "get_stock_price", "description": "Gets the stock price for a ticker.", "input_schema": { "type": "object", "properties": { "ticker": { "type": "string" } }, "required": ["ticker"] } } The good description clearly explains what the tool does, when to use it, what data it returns, and what the ticker parameter means. The poor description is too brief and leaves Claude with many open questions about the tool’s behavior and usage.

​ Controlling Claude’s output ​ Forcing tool use In some cases, you may want Claude to use a specific tool to answer the user’s question, even if Claude thinks it can provide an answer without using a tool. You can do this by specifying the tool in the tool_choice field like so:

tool_choice = {"type": "tool", "name": "get_weather"} When working with the tool_choice parameter, we have four possible options:

auto allows Claude to decide whether to call any provided tools or not. This is the default value when tools are provided. any tells Claude that it must use one of the provided tools, but doesn’t force a particular tool. tool allows us to force Claude to always use a particular tool. none prevents Claude from using any tools. This is the default value when no tools are provided. This diagram illustrates how each option works:

Note that when you have tool_choice as any or tool, we will prefill the assistant message to force a tool to be used. This means that the models will not emit a chain-of-thought text content block before tool_use content blocks, even if explicitly asked to do so.

Our testing has shown that this should not reduce performance. If you would like to keep chain-of-thought (particularly with Opus) while still requesting that the model use a specific tool, you can use {"type": "auto"} for tool_choice (the default) and add explicit instructions in a user message. For example: What's the weather like in London? Use the get_weather tool in your response.

​ JSON output Tools do not necessarily need to be client-side functions — you can use tools anytime you want the model to return JSON output that follows a provided schema. For example, you might use a record_summary tool with a particular schema. See tool use examples for a full working example.

​ Chain of thought When using tools, Claude will often show its “chain of thought”, i.e. the step-by-step reasoning it uses to break down the problem and decide which tools to use. The Claude 3 Opus model will do this if tool_choice is set to auto (this is the default value, see Forcing tool use), and Sonnet and Haiku can be prompted into doing it.

For example, given the prompt “What’s the weather like in San Francisco right now, and what time is it there?”, Claude might respond with:

JSON

{ "role": "assistant", "content": [ { "type": "text", "text": "<thinking>To answer this question, I will: 1. Use the get_weather tool to get the current weather in San Francisco. 2. Use the get_time tool to get the current time in the America/Los_Angeles timezone, which covers San Francisco, CA.</thinking>" }, { "type": "tool_use", "id": "toolu_01A09q90qw90lq917835lq9", "name": "get_weather", "input": {"location": "San Francisco, CA"} } ] } This chain of thought gives insight into Claude’s reasoning process and can help you debug unexpected behavior.

With the Claude 3 Sonnet model, chain of thought is less common by default, but you can prompt Claude to show its reasoning by adding something like "Before answering, explain your reasoning step-by-step in tags." to the user message or system prompt.

It’s important to note that while the <thinking> tags are a common convention Claude uses to denote its chain of thought, the exact format (such as what this XML tag is named) may change over time. Your code should treat the chain of thought like any other assistant-generated text, and not rely on the presence or specific formatting of the <thinking> tags.

​ Parallel tool use By default, Claude may use multiple tools to answer a user query. You can disable this behavior by setting disable_parallel_tool_use=true in the tool_choice field.

When tool_choice type is auto, this ensures that Claude uses at most one tool When tool_choice type is any or tool, this ensures that Claude uses exactly one tool Parallel tool use with Claude 3.7 Sonnet

Claude 3.7 Sonnet may be less likely to make make parallel tool calls in a response, even when you have not set disable_parallel_tool_use. To work around this, we recommend enabling token-efficient tool use, which helps encourage Claude to use parallel tools.

If you prefer not to opt into the token-efficient tool use beta, you can also introduce a “batch tool” that can act as a meta-tool to wrap invocations to other tools simultaneously. We find that if this tool is present, the model will use it to simultaneously call multiple tools in parallel for you.

See this example in our cookbook for how to use this workaround.

​ Handling tool use and tool result content blocks When Claude decides to use one of the tools you’ve provided, it will return a response with a stop_reason of tool_use and one or more tool_use content blocks in the API response that include:

id: A unique identifier for this particular tool use block. This will be used to match up the tool results later. name: The name of the tool being used. input: An object containing the input being passed to the tool, conforming to the tool’s input_schema.

Example API response with a tool_use content block

When you receive a tool use response, you should:

Extract the name, id, and input from the tool_use block. Run the actual tool in your codebase corresponding to that tool name, passing in the tool input. Continue the conversation by sending a new message with the role of user, and a content block containing the tool_result type and the following information: tool_use_id: The id of the tool use request this is a result for. content: The result of the tool, as a string (e.g. "content": "15 degrees") or list of nested content blocks (e.g. "content": [{"type": "text", "text": "15 degrees"}]). These content blocks can use the text or image types. is_error (optional): Set to true if the tool execution resulted in an error.

Example of successful tool result

Example of tool result with images

Example of empty tool result

After receiving the tool result, Claude will use that information to continue generating a response to the original user prompt.

Differences from other APIs

Unlike APIs that separate tool use or use special roles like tool or function, Anthropic’s API integrates tools directly into the user and assistant message structure.

Messages contain arrays of text, image, tool_use, and tool_result blocks. user messages include client-side content and tool_result, while assistant messages contain AI-generated content and tool_use.

​ Troubleshooting errors There are a few different types of errors that can occur when using tools with Claude:

Tool execution error

Max tokens exceeded

Invalid tool name

<search_quality_reflection> tags

​ Tool use examples Here are a few code examples demonstrating various tool use patterns and techniques. For brevity’s sake, the tools are simple tools, and the tool descriptions are shorter than would be ideal to ensure best performance.

Single tool example

Multiple tool example

Missing information

Sequential tools

Chain of thought tool use

JSON mode

​ Pricing Tool use requests are priced the same as any other Claude API request, based on the total number of input tokens sent to the model (including in the tools parameter) and the number of output tokens generated.”

The additional tokens from tool use come from:

The tools parameter in API requests (tool names, descriptions, and schemas) tool_use content blocks in API requests and responses tool_result content blocks in API requests When you use tools, we also automatically include a special system prompt for the model which enables tool use. The number of tool use tokens required for each model are listed below (excluding the additional tokens listed above). Note that the table assumes at least 1 tool is provided. If no tools are provided, then a tool choice of none uses 0 additional system prompt tokens.

Model Tool choice Tool use system prompt token count Claude 3.7 Sonnet auto, none any, tool 346 tokens 313 tokens Claude 3.5 Sonnet (Oct) auto, none any, tool 346 tokens 313 tokens Claude 3 Opus auto, none any, tool 530 tokens 281 tokens Claude 3 Sonnet auto, none any, tool 159 tokens 235 tokens Claude 3 Haiku auto, none any, tool 264 tokens 340 tokens Claude 3.5 Sonnet (June) auto, none any, tool 294 tokens 261 tokens These token counts are added to your normal input and output tokens to calculate the total cost of a request. Refer to our models overview table for current per-model prices.

When you send a tool use prompt, just like any other API request, the response will output both input and output token counts as part of the reported usage metrics.