Define tools

Prerequisites

• Familiarity with the tool use overview
• A Claude API key and a working SDK or cURL setup

Specifying client tools

Client tools are specified in the tools top-level parameter of the API request. Anthropic-schema client tools, such as the bash and text editor tools, are declared by a date-versioned type; see each tool's page, linked from the Tool reference, for the fields it accepts. The computer use and browser use tools are client toolsets: a single entry with no name that declares a fixed set of member tools. A user-defined tool definition includes:

For the full set of optional properties available on any single tool definition, including cache_control, strict, defer_loading, and allowed_callers, see the Tool reference. A client toolset entry accepts cache_control and allowed_callers on the entry and sets defer_loading per member; see Client toolsets.

This tool, named get_weather, expects an input object with a required location string and an optional unit string that must be either "celsius" or "fahrenheit".

Tool use system prompt

When you call the Claude API with the tools parameter, the API constructs 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 for each tool description, more if the tool is complex.

• Prioritize descriptions, but consider using input_examples for complex tools. Clear descriptions are most important, but for tools with complex inputs, nested objects, or format-sensitive parameters, you can use the input_examples field to provide schema-validated examples. See Providing tool use examples for details.

• Consolidate related operations into fewer tools. Rather than creating a separate tool for every action (create_pr, review_pr, merge_pr), group them into a single tool with an action parameter. Fewer, more capable tools reduce selection ambiguity and make your tool surface easier for Claude to navigate.

• Use meaningful namespacing in tool names. When your tools span multiple services or resources, prefix names with the service (for example, github_list_prs, slack_send_message). This makes tool selection unambiguous as your library grows, and is especially important when using tool search.

• Design tool responses to return only high-signal information. Return semantic, stable identifiers (for example, slugs or UUIDs) rather than opaque internal references, and include only the fields Claude needs to reason about its next step. Bloated responses waste context and make it harder for Claude to extract what matters.

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.

Providing tool use examples

You can provide concrete examples of valid tool inputs to help Claude understand how to use your tools more effectively. This is particularly useful for complex tools with nested objects, optional parameters, or format-sensitive inputs.

Basic usage

Add an optional input_examples field to your tool definition with an array of example input objects. Each example must be valid according to the tool's input_schema:

ant messages create <<'YAML'
model: claude-opus-5
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
        unit:
          type: string
          enum: [celsius, fahrenheit]
          description: The unit of temperature
      required: [location]
    input_examples:
      - location: San Francisco, CA
        unit: fahrenheit
      - location: Tokyo, Japan
        unit: celsius
      - location: New York, NY  # 'unit' is optional
messages:
  - role: user
    content: What's the weather like in San Francisco?
YAML

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-5",
    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",
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The unit of temperature",
                    },
                },
                "required": ["location"],
            },
            "input_examples": [
                {"location": "San Francisco, CA", "unit": "fahrenheit"},
                {"location": "Tokyo, Japan", "unit": "celsius"},
                {
                    "location": "New York, NY"  # 'unit' is optional
                },
            ],
        }
    ],
    messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}],
)

print(response)

const client = new Anthropic();

const response = await client.messages.create({
  model: "claude-opus-5",
  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"
          },
          unit: {
            type: "string",
            enum: ["celsius", "fahrenheit"],
            description: "The unit of temperature"
          }
        },
        required: ["location"]
      },
      input_examples: [
        {
          location: "San Francisco, CA",
          unit: "fahrenheit"
        },
        {
          location: "Tokyo, Japan",
          unit: "celsius"
        },
        {
          location: "New York, NY"
          // Demonstrates that 'unit' is optional
        }
      ]
    }
  ],
  messages: [{ role: "user", content: "What's the weather like in San Francisco?" }]
});

console.log(response);

AnthropicClient client = new();

var parameters = new MessageCreateParams
{
    Model = Model.ClaudeOpus5,
    MaxTokens = 1024,
    Tools = [
        new ToolUnion(new Tool()
        {
            Name = "get_weather",
            Description = "Get the current weather in a given location",
            InputSchema = new InputSchema()
            {
                Properties = new Dictionary<string, JsonElement>
                {
                    ["location"] = JsonSerializer.SerializeToElement(new { type = "string", description = "The city and state, e.g. San Francisco, CA" }),
                    ["unit"] = JsonSerializer.SerializeToElement(new { type = "string", @enum = new[] { "celsius", "fahrenheit" }, description = "The unit of temperature" }),
                },
                Required = ["location"],
            },
            InputExamples =
            [
                new Dictionary<string, JsonElement>()
                {
                    { "location", JsonSerializer.SerializeToElement("San Francisco, CA") },
                    { "unit", JsonSerializer.SerializeToElement("fahrenheit") },
                },
                new Dictionary<string, JsonElement>()
                {
                    { "location", JsonSerializer.SerializeToElement("Tokyo, Japan") },
                    { "unit", JsonSerializer.SerializeToElement("celsius") },
                },
                new Dictionary<string, JsonElement>()
                {
                    { "location", JsonSerializer.SerializeToElement("New York, NY") },
                },
            ],
        }),
    ],
    Messages = [
        new() { Role = Role.User, Content = "What's the weather like in San Francisco?" }
    ]
};

var message = await client.Messages.Create(parameters);
Console.WriteLine(message);

client := anthropic.NewClient()

response, err := client.Messages.New(context.TODO(), anthropic.MessageNewParams{
	Model:     anthropic.ModelClaudeOpus5,
	MaxTokens: 1024,
	Tools: []anthropic.ToolUnionParam{
		{OfTool: &anthropic.ToolParam{
			Name:        "get_weather",
			Description: anthropic.String("Get the current weather in a given location"),
			InputSchema: anthropic.ToolInputSchemaParam{
				Properties: map[string]any{
					"location": map[string]any{
						"type":        "string",
						"description": "The city and state, e.g. San Francisco, CA",
					},
					"unit": map[string]any{
						"type":        "string",
						"enum":        []string{"celsius", "fahrenheit"},
						"description": "The unit of temperature",
					},
				},
				Required: []string{"location"},
			},
			InputExamples: []map[string]any{
				{
					"location": "San Francisco, CA",
					"unit":     "fahrenheit",
				},
				{
					"location": "Tokyo, Japan",
					"unit":     "celsius",
				},
				{
					"location": "New York, NY",
					// Demonstrates that 'unit' is optional
				},
			},
		}},
	},
	Messages: []anthropic.MessageParam{
		anthropic.NewUserMessage(anthropic.NewTextBlock("What's the weather like in San Francisco?")),
	},
})
if err != nil {
	log.Fatal(err)
}
fmt.Println(response.RawJSON())

import com.anthropic.models.messages.Tool;
import com.anthropic.models.messages.Tool.InputSchema;
// ...
void main() {
    AnthropicClient client = AnthropicOkHttpClient.fromEnv();

    MessageCreateParams params = MessageCreateParams.builder()
        .model(Model.CLAUDE_OPUS_5)
        .maxTokens(1024L)
        .addTool(Tool.builder()
            .name("get_weather")
            .description("Get the current weather in a given location")
            .inputSchema(InputSchema.builder()
                .properties(JsonValue.from(Map.of(
                    "location", Map.of(
                        "type", "string",
                        "description", "The city and state, e.g. San Francisco, CA"
                    ),
                    "unit", Map.of(
                        "type", "string",
                        "enum", List.of("celsius", "fahrenheit"),
                        "description", "The unit of temperature"
                    )
                )))
                .required(List.of("location"))
                .build())
            .putAdditionalProperty("input_examples", JsonValue.from(List.of(
                Map.of(
                    "location", "San Francisco, CA",
                    "unit", "fahrenheit"
                ),
                Map.of(
                    "location", "Tokyo, Japan",
                    "unit", "celsius"
                ),
                Map.of(
                    "location", "New York, NY"
                )
            )))
            .build())
        .addUserMessage("What's the weather like in San Francisco?")
        .build();

    Message response = client.messages().create(params);
    IO.println(response);
}

$client = new Client();

$message = $client->messages->create(
    maxTokens: 1024,
    messages: [
        ['role' => 'user', 'content' => "What's the weather like in San Francisco?"]
    ],
    model: 'claude-opus-5',
    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'
                    ],
                    'unit' => [
                        'type' => 'string',
                        'enum' => ['celsius', 'fahrenheit'],
                        'description' => 'The unit of temperature'
                    ]
                ],
                'required' => ['location']
            ],
            'input_examples' => [
                [
                    'location' => 'San Francisco, CA',
                    'unit' => 'fahrenheit'
                ],
                [
                    'location' => 'Tokyo, Japan',
                    'unit' => 'celsius'
                ],
                [
                    'location' => 'New York, NY'
                ]
            ]
        ]
    ],
);

client = Anthropic::Client.new

message = client.messages.create(
  model: "claude-opus-5",
  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"
          },
          unit: {
            type: "string",
            enum: ["celsius", "fahrenheit"],
            description: "The unit of temperature"
          }
        },
        required: ["location"]
      },
      input_examples: [
        {
          location: "San Francisco, CA",
          unit: "fahrenheit"
        },
        {
          location: "Tokyo, Japan",
          unit: "celsius"
        },
        {
          location: "New York, NY"
        }
      ]
    }
  ],
  messages: [
    { role: "user", content: "What's the weather like in San Francisco?" }
  ]
)
puts message

Examples are included in the prompt alongside your tool schema, showing Claude concrete patterns for well-formed tool calls. This helps Claude understand when to include optional parameters, what formats to use, and how to structure complex inputs.

Requirements and limitations

• Schema validation - Each example must be valid according to the tool's input_schema. Invalid examples return a 400 error
• Not supported for server-side tools or client toolsets - Input examples work on user-defined and Anthropic-schema client tools other than the computer use and browser use toolsets, but not on server tools such as web search or code execution
• Token cost - Examples add to prompt tokens: ~20–50 tokens for simple examples, ~100–200 tokens for complex nested objects

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 would otherwise answer directly without calling a tool. You can do this by specifying the tool in the tool_choice field of the request.

Not every model and setting supports forced tool use. Where it isn't supported, tool_choice: {"type": "any"} and tool_choice: {"type": "tool", "name": "..."} fail, while tool_choice: {"type": "auto"} (the default) and tool_choice: {"type": "none"} still work:

On models that support it, the highlighted lines are the only difference from a standard tool use request:

ant messages create <<'YAML'
model: claude-opus-5
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]
tool_choice:
  type: tool
  name: get_weather
messages:
  - role: user
    content: What's the weather like in San Francisco?
YAML

client = anthropic.Anthropic()

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"],
        },
    }
]

response = client.messages.create(
    model="claude-opus-5",
    max_tokens=1024,
    tools=tools,
    tool_choice={"type": "tool", "name": "get_weather"},
    messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}],
)

print(response)

const client = new Anthropic();

const response = await client.messages.create({
  model: "claude-opus-5",
  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"]
      }
    }
  ],
  tool_choice: { type: "tool", name: "get_weather" },
  messages: [{ role: "user", content: "What's the weather like in San Francisco?" }]
});

console.log(response);

AnthropicClient client = new();

var parameters = new MessageCreateParams
{
    Model = Model.ClaudeOpus5,
    MaxTokens = 1024,
    Tools = [
        new ToolUnion(new Tool()
        {
            Name = "get_weather",
            Description = "Get the current weather in a given location",
            InputSchema = new InputSchema()
            {
                Properties = new Dictionary<string, JsonElement>
                {
                    ["location"] = JsonSerializer.SerializeToElement(new { type = "string", description = "The city and state, e.g. San Francisco, CA" }),
                },
                Required = ["location"],
            },
        }),
    ],
    ToolChoice = new ToolChoiceTool { Name = "get_weather" },
    Messages = [
        new() { Role = Role.User, Content = "What's the weather like in San Francisco?" }
    ]
};

var message = await client.Messages.Create(parameters);
Console.WriteLine(message);

client := anthropic.NewClient()

response, err := client.Messages.New(context.TODO(), anthropic.MessageNewParams{
	Model:     anthropic.ModelClaudeOpus5,
	MaxTokens: 1024,
	Tools: []anthropic.ToolUnionParam{
		{OfTool: &anthropic.ToolParam{
			Name:        "get_weather",
			Description: anthropic.String("Get the current weather in a given location"),
			InputSchema: anthropic.ToolInputSchemaParam{
				Properties: map[string]any{
					"location": map[string]any{
						"type":        "string",
						"description": "The city and state, e.g. San Francisco, CA",
					},
				},
				Required: []string{"location"},
			},
		}},
	},
	ToolChoice: anthropic.ToolChoiceUnionParam{OfTool: &anthropic.ToolChoiceToolParam{Name: "get_weather"}},
	Messages: []anthropic.MessageParam{
		anthropic.NewUserMessage(anthropic.NewTextBlock("What's the weather like in San Francisco?")),
	},
})
if err != nil {
	log.Fatal(err)
}
fmt.Println(response.RawJSON())

import com.anthropic.models.messages.Tool;
import com.anthropic.models.messages.Tool.InputSchema;
import com.anthropic.models.messages.ToolChoice;
import com.anthropic.models.messages.ToolChoiceTool;
// ...
void main() {
    AnthropicClient client = AnthropicOkHttpClient.fromEnv();

    MessageCreateParams params = MessageCreateParams.builder()
        .model(Model.CLAUDE_OPUS_5)
        .maxTokens(1024L)
        .addTool(Tool.builder()
            .name("get_weather")
            .description("Get the current weather in a given location")
            .inputSchema(InputSchema.builder()
                .properties(JsonValue.from(Map.of(
                    "location", Map.of(
                        "type", "string",
                        "description", "The city and state, e.g. San Francisco, CA"
                    )
                )))
                .required(List.of("location"))
                .build())
            .build())
        .toolChoice(ToolChoice.ofTool(ToolChoiceTool.builder()
            .name("get_weather")
            .build()))
        .addUserMessage("What's the weather like in San Francisco?")
        .build();

    Message response = client.messages().create(params);
    IO.println(response);
}

$client = new Client();

$message = $client->messages->create(
    maxTokens: 1024,
    messages: [
        ['role' => 'user', 'content' => "What's the weather like in San Francisco?"]
    ],
    model: 'claude-opus-5',
    toolChoice: ['type' => 'tool', 'name' => 'get_weather'],
    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']
            ]
        ]
    ],
);

client = Anthropic::Client.new

message = client.messages.create(
  model: "claude-opus-5",
  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"]
      }
    }
  ],
  tool_choice: { type: "tool", name: "get_weather" },
  messages: [
    { role: "user", content: "What's the weather like in San Francisco?" }
  ]
)
puts message

When working with the tool_choice parameter, there are 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 forces 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, the API prefills the assistant message to force a tool to be used. This means that the models will not emit a natural language response or explanation before tool_use content blocks, even if explicitly asked to do so.

Testing has shown that this should not reduce performance. If you would like the model to provide natural language context or explanations 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.

On models that support forced tool use, combine tool_choice: {"type": "any"} with strict tool use to guarantee both that one of your tools is called and that the tool inputs strictly follow your schema. Set strict: true on your tool definitions to enable schema validation.

Model responses with tools

When using tools, Claude often comments on what it's doing or responds naturally to the user before calling tools.

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:

{
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "I'll help you check the current weather and time in San Francisco."
    },
    {
      "type": "tool_use",
      "id": "toolu_01A09q90qw90lq917835lq9",
      "name": "get_weather",
      "input": { "location": "San Francisco, CA" }
    }
  ]
}

This natural response style helps users understand what Claude is doing and creates a more conversational interaction. You can guide the style and content of these responses through your system prompts and by providing <examples> in your prompts.

It's important to note that Claude may use various phrasings and approaches when explaining its actions. Your code should treat these responses like any other assistant-generated text, and not rely on specific formatting conventions.

Next steps