Call a tool with the Converse API - Amazon Bedrock

Call a tool with the Converse API

To let a model use a tool to complete a response for a message, you send the message and the definitions for one or more tools to the model. If the model determines that one of the tools can help generate a response, it returns a request for you to use the tool and send the tool results back to the model. The model then uses the results to generate a response to the original message.

The following steps show how to use a tool with the Converse API. For example code, see Converse API tool use examples.

Step 1: Send the message and tool definition

To send the message and tool definition, you use the Converse or ConverseStream (for streaming responses) operations.

Note

Meta has specific recommendations for creating prompts that use tools with Llama 3.1 (or later) models. For more information, see JSON based tool calling in the Meta documentation.

The definition of the tool is a JSON schema that you pass in the toolConfig (ToolConfiguration) request parameter to the Converse operation. For information about the schema, see JSON schema. The following is an example schema for a tool that gets the most popular song played on a radio station.

{ "tools": [ { "toolSpec": { "name": "top_song", "description": "Get the most popular song played on a radio station.", "inputSchema": { "json": { "type": "object", "properties": { "sign": { "type": "string", "description": "The call sign for the radio station for which you want the most popular song. Example calls signs are WZPZ and WKRP." } }, "required": [ "sign" ] } } } } ] }

In the same request, you also pass a user message in the messages (Message) request parameter.

[ { "role": "user", "content": [ { "text": "What is the most popular song on WZPZ?" } ] } ]

If you are using an Anthropic Claude 3 model, you can force the use of a tool by specifying the toolChoice (ToolChoice) field in the toolConfig request parameter. Forcing the use of a tool is useful for testing your tool during development. The following example shows how to force the use of a tool called top_song.

{"tool" : {"name" : "top_song"}}

For information about other parameters that you can pass, see Carry out a conversation with the Converse API operations.

Step 2: Get the tool request from the model

When you invoke the Converse operation with the message and tool definition, the model uses the tool definition to determine if the tool is needed to answer the message. For example, if your chat app user sends the message What's the most popular song on WZPZ?, the model matches the message with the schema in the top_song tool definition and determines that the tool can help generate a response.

When the model decides that it needs a tool to generate a response, the model sets the stopReason response field to tool_use. The response also identifies the tool (top_song) that the model wants you to run and the radio station (WZPZ) that it wants you to query with the tool. Information about the requested tool is in the message that the model returns in the output (ConverseOutput) field. Specifically, the toolUse (ToolUseBlock) field. You use the toolUseId field to identify the tool request in later calls.

The following example shows the response from Converse when you pass the message discussed in Step 1: Send the message and tool definition.

{ "output": { "message": { "role": "assistant", "content": [ { "toolUse": { "toolUseId": "tooluse_kZJMlvQmRJ6eAyJE5GIl7Q", "name": "top_song", "input": { "sign": "WZPZ" } } } ] } }, "stopReason": "tool_use" }

Step 3: Make the tool request for the model

From the toolUse field in the model response, use the name field to identify the name of the tool. Then call your implementation of the tool and pass the input parameters from the input field.

Next, construct a user message that includes a toolResult (ToolResultBlock) content block. In the content block, include the response from the tool and the ID for the tool request that you got in the previous step.

{ "role": "user", "content": [ { "toolResult": { "toolUseId": "tooluse_kZJMlvQmRJ6eAyJE5GIl7Q", "content": [ { "json": { "song": "Elemental Hotel", "artist": "8 Storey Hike" } } ] } } ] }

Should an error occur in the tool, such as a request for a non existant radio station, you can send error information to the model in the toolResult field. To indicate an error, specify error in the status field. The following example error is for when the tool can't find the radio station.

{ "role": "user", "content": [ { "toolResult": { "toolUseId": "tooluse_kZJMlvQmRJ6eAyJE5GIl7Q", "content": [ { "text": "Station WZPA not found." } ], "status": "error" } } ] }

Step 4: Get the model response

Continue the conversation with the model by including the user message that you created in the previous step in a call to Converse. The model then generates a response that answers the original message ( What's the most popular song on WZPZ?) with the information that you provided in the toolResult field of the message.

{ "output": { "message": { "role": "assistant", "content": [ { "text": "The most popular song on WZPZ is Elemental Hotel by 8 Storey Hike." } ] } }, "stopReason": "end_turn"