---
title: Programmatic tool calling
url: https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling
description: Let Claude call your tools from code in the code execution container, cutting model round trips and token use in multi-tool workflows.
---
## Compatibility
- [ZDR](https://platform.claude.com/docs/en/manage-claude/api-and-data-retention): not eligible
- Supported models: `claude-fable-5-1`, `claude-mythos-5-1`, `claude-fable-5`, `claude-mythos-5`, `claude-opus-5`, `claude-opus-4-8`, `claude-opus-4-7`, `claude-opus-4-6`, `claude-opus-4-5-20251101`, `claude-sonnet-5`, `claude-sonnet-4-6`, `claude-sonnet-4-5-20250929`
- Platforms: Claude API, Claude Platform on AWS, Microsoft Foundry [1]; not available on Amazon Bedrock, Google Cloud
- Programmatic tool calling requires the code execution tool with the `code_execution_20260120` or later [tool version](https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool#tool-versions).
- Claude Haiku 4.5 accepts the `code_execution_20260120` and later tool versions but doesn't support programmatic tool calling.
1. On [Microsoft Foundry](https://platform.claude.com/docs/en/build-with-claude/claude-in-microsoft-foundry), programmatic tool calling requires a [Hosted on Anthropic deployment](https://platform.claude.com/docs/en/build-with-claude/claude-in-microsoft-foundry#additional-features-not-supported-when-hosted-on-azure).
Programmatic tool calling allows Claude to write code that calls your tools programmatically within a [code execution](https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool) container, rather than requiring round trips through the model for each tool invocation. This reduces latency for multi-tool workflows and decreases token consumption by allowing Claude to filter or process data before it reaches the model's context window. On agentic search benchmarks like [BrowseComp](https://arxiv.org/abs/2504.12516) and [DeepSearchQA](https://github.com/google-deepmind/deepsearchqa), which test multistep web research and complex information retrieval, adding programmatic tool calling on top of basic search tools improved performance by an average of 11% while using 24% fewer input tokens (see [Improved web search with dynamic filtering](https://claude.com/blog/improved-web-search-with-dynamic-filtering)).
Consider checking budget compliance across 20 employees: the traditional approach requires 20 separate model round-trips, pulling thousands of expense line items into the context along the way. With programmatic tool calling, a single script runs all 20 lookups, filters the results, and returns only the employees who exceeded their limits, shrinking what Claude needs to reason over from hundreds of kilobytes down to a handful of lines.
For a deeper look at the inference and context costs that programmatic tool calling addresses, see [Advanced tool use](https://www.anthropic.com/engineering/advanced-tool-use).
Programmatic tool calling requires the [code execution tool](https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool) with tool version `code_execution_20260120` or later.
## Quick start
Here's an example where Claude programmatically queries a database multiple times and aggregates results. Adding `allowed_callers: ["code_execution_20260120"]` to a tool definition is what makes that tool callable from within code execution (see [The `allowed_callers` field](https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling#the-allowed-callers-field)):
```bash cURL
curl https://api.anthropic.com/v1/messages \
--header "x-api-key: $ANTHROPIC_API_KEY" \
--header "anthropic-version: 2023-06-01" \
--header "content-type: application/json" \
--data '{
"model": "claude-opus-5",
"max_tokens": 4096,
"messages": [
{
"role": "user",
"content": "Query sales data for the West, East, and Central regions, then tell me which region had the highest revenue"
}
],
"tools": [
{
"type": "code_execution_20260120",
"name": "code_execution"
},
{
"name": "query_database",
"description": "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
"input_schema": {
"type": "object",
"properties": {
"sql": {
"type": "string",
"description": "SQL query to execute"
}
},
"required": ["sql"]
},
"allowed_callers": ["code_execution_20260120"]
}
]
}'
```
```bash CLI
ant messages create <<'YAML'
model: claude-opus-5
max_tokens: 4096
messages:
- role: user
content: >-
Query sales data for the West, East, and Central regions, then
tell me which region had the highest revenue
tools:
- type: code_execution_20260120
name: code_execution
- name: query_database
description: >-
Execute a SQL query against the sales database. Returns a list
of rows as JSON objects.
input_schema:
type: object
properties:
sql:
type: string
description: SQL query to execute
required:
- sql
allowed_callers:
- code_execution_20260120
YAML
```
```python Python
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-opus-5",
max_tokens=4096,
messages=[
{
"role": "user",
"content": "Query sales data for the West, East, and Central regions, then tell me which region had the highest revenue",
}
],
tools=[
{"type": "code_execution_20260120", "name": "code_execution"},
{
"name": "query_database",
"description": "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
"input_schema": {
"type": "object",
"properties": {
"sql": {"type": "string", "description": "SQL query to execute"}
},
"required": ["sql"],
},
"allowed_callers": ["code_execution_20260120"],
},
],
)
print(response)
```
```typescript TypeScript
const client = new Anthropic();
const response = await client.messages.create({
model: "claude-opus-5",
max_tokens: 4096,
messages: [
{
role: "user",
content:
"Query sales data for the West, East, and Central regions, then tell me which region had the highest revenue"
}
],
tools: [
{
type: "code_execution_20260120",
name: "code_execution"
},
{
name: "query_database",
description:
"Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
input_schema: {
type: "object" as const,
properties: {
sql: {
type: "string",
description: "SQL query to execute"
}
},
required: ["sql"]
},
allowed_callers: ["code_execution_20260120"]
}
]
});
console.log(response);
```
```csharp C#
AnthropicClient client = new();
var parameters = new MessageCreateParams
{
Model = Model.ClaudeOpus5,
MaxTokens = 4096,
Messages = [
new() {
Role = Role.User,
Content = "Query sales data for the West, East, and Central regions, then tell me which region had the highest revenue"
}
],
Tools = [
new CodeExecutionTool20260120(),
new ToolUnion(new Tool()
{
Name = "query_database",
Description = "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
InputSchema = new InputSchema()
{
Properties = new Dictionary
{
["sql"] = JsonSerializer.SerializeToElement(new { type = "string", description = "SQL query to execute" }),
},
Required = ["sql"],
},
AllowedCallers = ["code_execution_20260120"]
}),
]
};
var message = await client.Messages.Create(parameters);
Console.WriteLine(message);
```
```go Go
client := anthropic.NewClient()
response, err := client.Messages.New(context.TODO(), anthropic.MessageNewParams{
Model: anthropic.ModelClaudeOpus5,
MaxTokens: 4096,
Messages: []anthropic.MessageParam{
anthropic.NewUserMessage(anthropic.NewTextBlock("Query sales data for the West, East, and Central regions, then tell me which region had the highest revenue")),
},
Tools: []anthropic.ToolUnionParam{
{OfCodeExecutionTool20260120: &anthropic.CodeExecutionTool20260120Param{}},
{OfTool: &anthropic.ToolParam{
Name: "query_database",
Description: anthropic.String("Execute a SQL query against the sales database. Returns a list of rows as JSON objects."),
InputSchema: anthropic.ToolInputSchemaParam{
Properties: map[string]any{
"sql": map[string]any{
"type": "string",
"description": "SQL query to execute",
},
},
Required: []string{"sql"},
},
AllowedCallers: []string{"code_execution_20260120"},
}},
},
})
if err != nil {
log.Fatal(err)
}
fmt.Println(response.RawJSON())
```
```java Java
import com.anthropic.models.messages.CodeExecutionTool20260120;
// ...
void main() {
AnthropicClient client = AnthropicOkHttpClient.fromEnv();
MessageCreateParams params = MessageCreateParams.builder()
.model(Model.CLAUDE_OPUS_5)
.maxTokens(4096L)
.addUserMessage("Query sales data for the West, East, and Central regions, then tell me which region had the highest revenue")
.addTool(CodeExecutionTool20260120.builder().build())
.addTool(Tool.builder()
.name("query_database")
.description("Execute a SQL query against the sales database. Returns a list of rows as JSON objects.")
.inputSchema(InputSchema.builder()
.properties(JsonValue.from(Map.of(
"sql", Map.of(
"type", "string",
"description", "SQL query to execute"
)
)))
.putAdditionalProperty("required", JsonValue.from(List.of("sql")))
.build())
.allowedCallers(List.of(Tool.AllowedCaller.of("code_execution_20260120")))
.build())
.build();
Message response = client.messages().create(params);
IO.println(response);
}
```
```php PHP
$client = new Client();
$message = $client->messages->create(
maxTokens: 4096,
messages: [
['role' => 'user', 'content' => 'Query sales data for the West, East, and Central regions, then tell me which region had the highest revenue'],
],
model: 'claude-opus-5',
tools: [
[
'type' => 'code_execution_20260120',
'name' => 'code_execution',
],
[
'name' => 'query_database',
'description' => 'Execute a SQL query against the sales database. Returns a list of rows as JSON objects.',
'input_schema' => [
'type' => 'object',
'properties' => [
'sql' => [
'type' => 'string',
'description' => 'SQL query to execute',
],
],
'required' => ['sql'],
],
'allowed_callers' => ['code_execution_20260120'],
],
],
);
echo $message;
```
```ruby Ruby
client = Anthropic::Client.new
message = client.messages.create(
model: "claude-opus-5",
max_tokens: 4096,
messages: [
{
role: "user",
content: "Query sales data for the West, East, and Central regions, then tell me which region had the highest revenue"
}
],
tools: [
{
type: "code_execution_20260120",
name: "code_execution"
},
{
name: "query_database",
description: "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
input_schema: {
type: "object",
properties: {
sql: {
type: "string",
description: "SQL query to execute"
}
},
required: ["sql"]
},
allowed_callers: ["code_execution_20260120"]
}
]
)
puts message
```
The response stops with `stop_reason: "tool_use"`, a `container` ID, and a `tool_use` block for `query_database` whose `caller` field identifies the code execution run that called it. Return the result as shown in [Step 3 of the example workflow](https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling#step-3-provide-tool-result) so the code can finish.
## How programmatic tool calling works
When you configure a tool to be callable from code execution and Claude determines that tool is needed:
1. Claude writes Python code that invokes the tool as a function, potentially including multiple tool calls and pre/post-processing logic
2. Claude runs this code in a sandboxed container through code execution
3. When a tool function is called, code execution pauses and the API returns a `tool_use` block
4. You provide the tool result, and code execution continues (intermediate results are not loaded into Claude's context window)
5. Once all code execution completes, Claude receives the final output and continues working on the task
This approach is particularly useful for:
* **Large data processing:** Filter or aggregate tool results before they reach Claude's context
* **Multistep workflows:** Save tokens and latency by calling tools serially or in a loop without sampling Claude in-between tool calls
* **Conditional logic:** Make decisions based on intermediate tool results
Tools that allow a code execution caller are exposed to Claude's code as async Python functions, so Claude can run them in parallel with `asyncio.gather`. Each function takes a single dict of arguments and returns a string: the text of the `tool_result` you send back. Claude's code awaits these functions with top-level `await` and parses results that it needs as structured data, for example `rows = json.loads(await query_database({"sql": ""}))`.
## Core concepts
### The `allowed_callers` field
The `allowed_callers` field specifies which contexts can invoke a tool:
```json
{
"name": "query_database",
"description": "Execute a SQL query against the database",
"input_schema": {
// ...
},
"allowed_callers": ["code_execution_20260120"]
}
```
**Possible values:**
* `["direct"]` - Claude is guided to call this tool directly (default if omitted)
* `["code_execution_20260120"]` - Claude is guided to call this tool only from within code execution
* `["direct", "code_execution_20260120"]` - Claude may call this tool directly or from within code execution
Both `"code_execution_20260120"` and `"code_execution_20260521"` are accepted in `allowed_callers` and are interchangeable: a request using either code-execution tool version satisfies tools that list either caller. Response blocks always tag the caller as `code_execution_20260120` regardless of which version the request declared.
Choose either `["direct"]` or `["code_execution_20260120"]` for each tool rather than enabling both, as this provides clearer guidance to Claude for how best to use the tool.
`allowed_callers` controls how the tool is presented to Claude and is validated against `tool_choice`, but it is not a hard API-level block on direct invocation. Claude is strongly guided to respect it, but your client should still be prepared to handle a direct `tool_use` for any tool it defines. Do not rely on `allowed_callers` as a security boundary.
### The `caller` field in responses
Every tool use block includes a `caller` field indicating how it was invoked:
**Direct invocation (traditional tool use):**
```json
{
"type": "tool_use",
"id": "toolu_abc123",
"name": "query_database",
"input": { "sql": "" },
"caller": { "type": "direct" }
}
```
**Programmatic invocation:**
```json
{
"type": "tool_use",
"id": "toolu_xyz789",
"name": "query_database",
"input": { "sql": "" },
"caller": {
"type": "code_execution_20260120",
"tool_id": "srvtoolu_abc123"
}
}
```
The `tool_id` is the `id` of the code execution `server_tool_use` block that made the call, so you can match each programmatic `tool_use` to the code execution run that produced it.
### Container lifecycle
Programmatic tool calling uses the same containers as code execution:
* **Container creation:** A new container is created for each request unless you reuse an existing one
* **Container ID:** Returned in responses in the `container` field, along with an `expires_at` timestamp
* **Reuse:** Pass the container ID back on the next request to keep state. While a programmatic tool call is waiting for your result, the container ID is required on that request, not optional: the API rejects the request without it.
* **Expiration:** `expires_at` tells you how long the container has left. Idle containers are currently reclaimed after about 5 minutes, and no container can be reused more than 30 days after it was created.
While Claude's code is waiting for a programmatic tool result, the pending call times out after about 4 minutes and raises a `TimeoutError` inside the code. Return each tool result well before the `expires_at` timestamp on the paused response. See [Container expiration during tool call](https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling#container-expiration-during-tool-call).
## Example workflow
Here's how a complete programmatic tool calling flow works:
### Step 1: Initial request
Send a request with code execution and a tool that allows programmatic calling. To enable programmatic calling, add the `allowed_callers` field to your tool definition.
Provide detailed descriptions of your tool's output format in the tool description. If you specify that the tool returns JSON, Claude attempts to deserialize and process the result in code. The more detail you provide about the output schema, the better Claude can handle the response programmatically.
The request shape is identical to the [Quick start](https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling#quick-start) example: include `code_execution` in your tools list, add `allowed_callers: ["code_execution_20260120"]` to any tool you want Claude to invoke from code, and send your user message. The remaining steps in this workflow use the user message `"Query customer purchase history from the last quarter and identify our top 5 customers by revenue"`.
### Step 2: API response with tool call
Claude writes code that calls your tool. The API pauses and returns:
```json Output
{
"role": "assistant",
"content": [
{
"type": "text",
"text": "I'll query the purchase history and analyze the results."
},
{
"type": "server_tool_use",
"id": "srvtoolu_abc123",
"name": "code_execution",
"input": {
"code": "import json\n\nrows = json.loads(await query_database({'sql': ''}))\ntop_customers = sorted(rows, key=lambda x: x['revenue'], reverse=True)[:5]\nprint(f'Top 5 customers: {top_customers}')"
}
},
{
"type": "tool_use",
"id": "toolu_def456",
"name": "query_database",
"input": { "sql": "" },
"caller": {
"type": "code_execution_20260120",
"tool_id": "srvtoolu_abc123"
}
}
],
"container": {
"id": "container_xyz789",
"expires_at": "2026-01-20T14:30:00Z"
},
"stop_reason": "tool_use"
}
```
### Step 3: Provide tool result
Send the full conversation history plus your tool result. Three details matter on this request:
* The user message that carries your result can contain only `tool_result` blocks. See [Message formatting restrictions](https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling#message-formatting-restrictions).
* Pass the `container` ID from the paused response. The API rejects a continuation that has pending programmatic tool calls but no container ID.
* Send the same `tools` array as the original request. The code execution tool must still be present for the paused code to resume, and the tools you send on this request are the definitions Claude and the running code can use for the rest of the turn.
```bash cURL
curl https://api.anthropic.com/v1/messages \
--header "x-api-key: $ANTHROPIC_API_KEY" \
--header "anthropic-version: 2023-06-01" \
--header "content-type: application/json" \
--data '{
"model": "claude-opus-5",
"max_tokens": 4096,
"container": "container_xyz789",
"messages": [
{
"role": "user",
"content": "Query customer purchase history from the last quarter and identify our top 5 customers by revenue"
},
{
"role": "assistant",
"content": [
{
"type": "text",
"text": "I'\''ll query the purchase history and analyze the results."
},
{
"type": "server_tool_use",
"id": "srvtoolu_abc123",
"name": "code_execution",
"input": {"code": "..."}
},
{
"type": "tool_use",
"id": "toolu_def456",
"name": "query_database",
"input": {"sql": ""},
"caller": {
"type": "code_execution_20260120",
"tool_id": "srvtoolu_abc123"
}
}
]
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": "toolu_def456",
"content": "[{\"customer_id\": \"C1\", \"revenue\": 45000}, {\"customer_id\": \"C2\", \"revenue\": 38000}]"
}
]
}
],
"tools": [
{
"type": "code_execution_20260120",
"name": "code_execution"
},
{
"name": "query_database",
"description": "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
"input_schema": {
"type": "object",
"properties": {
"sql": {
"type": "string",
"description": "SQL query to execute"
}
},
"required": ["sql"]
},
"allowed_callers": ["code_execution_20260120"]
}
]
}'
```
```bash CLI
ant messages create <<'YAML'
model: claude-opus-5
max_tokens: 4096
container: container_xyz789
messages:
- role: user
content: >-
Query customer purchase history from the last quarter and identify our
top 5 customers by revenue
- role: assistant
content:
- type: text
text: I'll query the purchase history and analyze the results.
- type: server_tool_use
id: srvtoolu_abc123
name: code_execution
input:
code: "..."
- type: tool_use
id: toolu_def456
name: query_database
input:
sql: ""
caller:
type: code_execution_20260120
tool_id: srvtoolu_abc123
- role: user
content:
- type: tool_result
tool_use_id: toolu_def456
content: >-
[{"customer_id": "C1", "revenue": 45000}, {"customer_id": "C2",
"revenue": 38000}, ...]
# Same tools array as the original request
tools:
- type: code_execution_20260120
name: code_execution
- name: query_database
description: >-
Execute a SQL query against the sales database. Returns a list
of rows as JSON objects.
input_schema:
type: object
properties:
sql:
type: string
description: SQL query to execute
required:
- sql
allowed_callers:
- code_execution_20260120
YAML
```
```python Python
response = client.messages.create(
model="claude-opus-5",
max_tokens=4096,
container="container_xyz789", # Reuse the container
messages=[
{
"role": "user",
"content": "Query customer purchase history from the last quarter and identify our top 5 customers by revenue",
},
{
"role": "assistant",
"content": [
{
"type": "text",
"text": "I'll query the purchase history and analyze the results.",
},
{
"type": "server_tool_use",
"id": "srvtoolu_abc123",
"name": "code_execution",
"input": {"code": "..."},
},
{
"type": "tool_use",
"id": "toolu_def456",
"name": "query_database",
"input": {"sql": ""},
"caller": {
"type": "code_execution_20260120",
"tool_id": "srvtoolu_abc123",
},
},
],
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": "toolu_def456",
"content": '[{"customer_id": "C1", "revenue": 45000}, {"customer_id": "C2", "revenue": 38000}, ...]',
}
],
},
],
# Same tools array as the original request
tools=[
{"type": "code_execution_20260120", "name": "code_execution"},
{
"name": "query_database",
"description": "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
"input_schema": {
"type": "object",
"properties": {
"sql": {"type": "string", "description": "SQL query to execute"}
},
"required": ["sql"],
},
"allowed_callers": ["code_execution_20260120"],
},
],
)
print(response)
```
```typescript TypeScript
const response = await client.messages.create({
model: "claude-opus-5",
max_tokens: 4096,
container: "container_xyz789", // Reuse the container
messages: [
{
role: "user",
content:
"Query customer purchase history from the last quarter and identify our top 5 customers by revenue"
},
{
role: "assistant",
content: [
{ type: "text", text: "I'll query the purchase history and analyze the results." },
{
type: "server_tool_use",
id: "srvtoolu_abc123",
name: "code_execution",
input: { code: "..." }
},
{
type: "tool_use",
id: "toolu_def456",
name: "query_database",
input: { sql: "" },
caller: {
type: "code_execution_20260120",
tool_id: "srvtoolu_abc123"
}
}
]
},
{
role: "user",
content: [
{
type: "tool_result",
tool_use_id: "toolu_def456",
content:
'[{"customer_id": "C1", "revenue": 45000}, {"customer_id": "C2", "revenue": 38000}, ...]'
}
]
}
],
// Same tools array as the original request
tools: [
{
type: "code_execution_20260120",
name: "code_execution"
},
{
name: "query_database",
description:
"Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
input_schema: {
type: "object" as const,
properties: {
sql: {
type: "string",
description: "SQL query to execute"
}
},
required: ["sql"]
},
allowed_callers: ["code_execution_20260120"]
}
]
});
console.log(response);
```
```csharp C#
AnthropicClient client = new();
var parameters = new MessageCreateParams
{
Model = Model.ClaudeOpus5,
MaxTokens = 4096,
Container = "container_xyz789",
Messages =
[
new()
{
Role = Role.User,
Content = "Query customer purchase history from the last quarter and identify our top 5 customers by revenue"
},
new()
{
Role = Role.Assistant,
Content = new ContentBlock[]
{
new TextBlock { Text = "I'll query the purchase history and analyze the results." },
new ServerToolUseBlock
{
Id = "srvtoolu_abc123",
Name = "code_execution",
Input = new { code = "..." }
},
new ToolUseBlock
{
Id = "toolu_def456",
Name = "query_database",
Input = new { sql = "" },
Caller = new ToolCaller
{
Type = "code_execution_20260120",
ToolId = "srvtoolu_abc123"
}
}
}
},
new()
{
Role = Role.User,
Content = new ContentBlockParam[]
{
new ToolResultBlockParam
{
ToolUseID = "toolu_def456",
Content = "[{\"customer_id\": \"C1\", \"revenue\": 45000}, {\"customer_id\": \"C2\", \"revenue\": 38000}, ...]"
}
}
}
],
// Same tools array as the original request
Tools = [
new CodeExecutionTool20260120(),
new ToolUnion(new Tool()
{
Name = "query_database",
Description = "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
InputSchema = new InputSchema()
{
Properties = new Dictionary
{
["sql"] = JsonSerializer.SerializeToElement(new { type = "string", description = "SQL query to execute" }),
},
Required = ["sql"],
},
AllowedCallers = ["code_execution_20260120"]
}),
]
};
var message = await client.Messages.Create(parameters);
Console.WriteLine(message);
```
```go Go
client := anthropic.NewClient()
response, err := client.Messages.New(context.TODO(), anthropic.MessageNewParams{
Model: anthropic.ModelClaudeOpus5,
MaxTokens: 4096,
Container: anthropic.MessageCreateParamsContainerUnion{
OfString: anthropic.String("container_xyz789"),
},
Messages: []anthropic.MessageParam{
anthropic.NewUserMessage(anthropic.NewTextBlock("Query customer purchase history from the last quarter and identify our top 5 customers by revenue")),
{
Role: anthropic.MessageParamRoleAssistant,
Content: []anthropic.ContentBlockParamUnion{
anthropic.NewTextBlock("I'll query the purchase history and analyze the results."),
{OfServerToolUse: &anthropic.ServerToolUseBlockParam{
ID: "srvtoolu_abc123",
Name: anthropic.ServerToolUseBlockParamNameCodeExecution,
Input: map[string]any{"code": "..."},
}},
{OfToolUse: &anthropic.ToolUseBlockParam{
ID: "toolu_def456",
Name: "query_database",
Input: map[string]any{"sql": ""},
Caller: anthropic.ServerToolUseBlockParamCallerUnion{
OfCodeExecution20260120: &anthropic.ServerToolCaller20260120Param{
ToolID: "srvtoolu_abc123",
},
},
}},
},
},
{
Role: anthropic.MessageParamRoleUser,
Content: []anthropic.ContentBlockParamUnion{
{OfToolResult: &anthropic.ToolResultBlockParam{
ToolUseID: "toolu_def456",
Content: []anthropic.ToolResultBlockParamContentUnion{
{OfText: &anthropic.TextBlockParam{
Text: `[{"customer_id": "C1", "revenue": 45000}, {"customer_id": "C2", "revenue": 38000}, ...]`,
}},
},
}},
},
},
},
// Same tools array as the original request
Tools: []anthropic.ToolUnionParam{
{OfCodeExecutionTool20260120: &anthropic.CodeExecutionTool20260120Param{}},
{OfTool: &anthropic.ToolParam{
Name: "query_database",
Description: anthropic.String("Execute a SQL query against the sales database. Returns a list of rows as JSON objects."),
InputSchema: anthropic.ToolInputSchemaParam{
Properties: map[string]any{
"sql": map[string]any{
"type": "string",
"description": "SQL query to execute",
},
},
Required: []string{"sql"},
},
AllowedCallers: []string{"code_execution_20260120"},
}},
},
})
if err != nil {
log.Fatal(err)
}
fmt.Println(response.RawJSON())
```
```java Java
import com.anthropic.models.messages.CodeExecutionTool20260120;
// ...
void main() {
AnthropicClient client = AnthropicOkHttpClient.fromEnv();
MessageCreateParams params = MessageCreateParams.builder()
.model(Model.CLAUDE_OPUS_5)
.maxTokens(4096L)
.container("container_xyz789")
.addUserMessage("Query customer purchase history from the last quarter and identify our top 5 customers by revenue")
.addAssistantMessageOfBlockParams(List.of(
ContentBlockParam.ofText(
TextBlockParam.builder()
.text("I'll query the purchase history and analyze the results.")
.build()),
ContentBlockParam.ofServerToolUse(
ServerToolUseBlockParam.builder()
.id("srvtoolu_abc123")
.name("code_execution")
.input(JsonValue.from(Map.of("code", "...")))
.build()),
ContentBlockParam.ofToolUse(
ToolUseBlockParam.builder()
.id("toolu_def456")
.name("query_database")
.input(JsonValue.from(Map.of("sql", "")))
.codeExecution20260120Caller("srvtoolu_abc123")
.build())
))
.addUserMessageOfBlockParams(List.of(
ContentBlockParam.ofToolResult(
ToolResultBlockParam.builder()
.toolUseId("toolu_def456")
.content("[{\"customer_id\": \"C1\", \"revenue\": 45000}, {\"customer_id\": \"C2\", \"revenue\": 38000}, ...]")
.build())
))
// Same tools array as the original request
.addTool(CodeExecutionTool20260120.builder().build())
.addTool(Tool.builder()
.name("query_database")
.description("Execute a SQL query against the sales database. Returns a list of rows as JSON objects.")
.inputSchema(InputSchema.builder()
.properties(JsonValue.from(Map.of(
"sql", Map.of(
"type", "string",
"description", "SQL query to execute"
)
)))
.putAdditionalProperty("required", JsonValue.from(List.of("sql")))
.build())
.allowedCallers(List.of(Tool.AllowedCaller.of("code_execution_20260120")))
.build())
.build();
Message response = client.messages().create(params);
IO.println(response);
}
```
```php PHP
$client = new Client();
$message = $client->messages->create(
maxTokens: 4096,
messages: [
[
'role' => 'user',
'content' => 'Query customer purchase history from the last quarter and identify our top 5 customers by revenue',
],
[
'role' => 'assistant',
'content' => [
[
'type' => 'text',
'text' => "I'll query the purchase history and analyze the results.",
],
[
'type' => 'server_tool_use',
'id' => 'srvtoolu_abc123',
'name' => 'code_execution',
'input' => ['code' => '...'],
],
[
'type' => 'tool_use',
'id' => 'toolu_def456',
'name' => 'query_database',
'input' => ['sql' => ''],
'caller' => [
'type' => 'code_execution_20260120',
'tool_id' => 'srvtoolu_abc123',
],
],
],
],
[
'role' => 'user',
'content' => [
[
'type' => 'tool_result',
'tool_use_id' => 'toolu_def456',
'content' => '[{"customer_id": "C1", "revenue": 45000}, {"customer_id": "C2", "revenue": 38000}, ...]',
],
],
],
],
model: 'claude-opus-5',
container: 'container_xyz789',
// Same tools array as the original request
tools: [
[
'type' => 'code_execution_20260120',
'name' => 'code_execution',
],
[
'name' => 'query_database',
'description' => 'Execute a SQL query against the sales database. Returns a list of rows as JSON objects.',
'input_schema' => [
'type' => 'object',
'properties' => [
'sql' => [
'type' => 'string',
'description' => 'SQL query to execute',
],
],
'required' => ['sql'],
],
'allowed_callers' => ['code_execution_20260120'],
],
],
);
echo $message;
```
```ruby Ruby
require "anthropic"
client = Anthropic::Client.new
message = client.messages.create(
model: "claude-opus-5",
max_tokens: 4096,
container: "container_xyz789",
messages: [
{
role: "user",
content: "Query customer purchase history from the last quarter and identify our top 5 customers by revenue"
},
{
role: "assistant",
content: [
{
type: "text",
text: "I'll query the purchase history and analyze the results."
},
{
type: "server_tool_use",
id: "srvtoolu_abc123",
name: "code_execution",
input: { code: "..." }
},
{
type: "tool_use",
id: "toolu_def456",
name: "query_database",
input: { sql: "" },
caller: {
type: "code_execution_20260120",
tool_id: "srvtoolu_abc123"
}
}
]
},
{
role: "user",
content: [
{
type: "tool_result",
tool_use_id: "toolu_def456",
content: '[{"customer_id": "C1", "revenue": 45000}, {"customer_id": "C2", "revenue": 38000}, ...]'
}
]
}
],
# Same tools array as the original request
tools: [
{
type: "code_execution_20260120",
name: "code_execution"
},
{
name: "query_database",
description: "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.",
input_schema: {
type: "object",
properties: {
sql: {
type: "string",
description: "SQL query to execute"
}
},
required: ["sql"]
},
allowed_callers: ["code_execution_20260120"]
}
]
)
puts message
```
### Step 4: Next tool call or completion
The code picks up where it paused and processes your result. Each continuation response either pauses again with more programmatic `tool_use` blocks, or completes the code execution and lets Claude continue the turn (Step 5). Check `stop_reason` and each `tool_use` block's `caller` to tell the two apart: a response that pauses for you has `stop_reason: "tool_use"` and a `tool_use` block whose `caller` names a code execution version, and you repeat Step 3 with a `tool_result` for every pending programmatic call in one user message.
### Step 5: Final response
Once the code execution completes, Claude provides the final response:
```json Output
{
"content": [
{
"type": "code_execution_tool_result",
"tool_use_id": "srvtoolu_abc123",
"content": {
"type": "code_execution_result",
"stdout": "Top 5 customers: [{'customer_id': 'C1', 'revenue': 45000}, {'customer_id': 'C2', 'revenue': 38000}, {'customer_id': 'C5', 'revenue': 32000}, {'customer_id': 'C8', 'revenue': 28500}, {'customer_id': 'C3', 'revenue': 24000}]",
"stderr": "",
"return_code": 0,
"content": []
}
},
{
"type": "text",
"text": "I've analyzed the purchase history from last quarter. Your top 5 customers generated $167,500 in total revenue, with Customer C1 leading at $45,000."
}
],
"stop_reason": "end_turn"
}
```
## Advanced patterns
### Batch processing with loops
Claude can write code that processes multiple items efficiently:
```python
regions = ["West", "East", "Central", "North", "South"]
results = {}
for region in regions:
rows = json.loads(await query_database({"sql": f""}))
results[region] = sum(row["revenue"] for row in rows)
# Process results programmatically
top_region = max(results.items(), key=lambda x: x[1])
print(f"Top region: {top_region[0]} with ${top_region[1]:,} in revenue")
```
This pattern:
* Reduces model round-trips from N (one per region) to 1
* Processes large result sets programmatically before returning to Claude
* Saves tokens by only returning aggregated conclusions instead of raw data
### Early termination
Claude can stop processing as soon as success criteria are met:
```python
endpoints = ["us-east", "eu-west", "apac"]
for endpoint in endpoints:
status = await check_health({"endpoint": endpoint})
if status == "healthy":
print(f"Found healthy endpoint: {endpoint}")
break # Stop early, don't check remaining
```
### Conditional tool selection
```python
path = "/tmp/example.txt"
file_info = json.loads(await get_file_info({"path": path}))
if file_info["size"] < 10000:
content = await read_full_file({"path": path})
else:
content = await read_file_summary({"path": path})
print(content)
```
### Data filtering
```python
server_id = "srv-01"
log_text = await fetch_logs({"server_id": server_id})
errors = [line for line in log_text.splitlines() if "ERROR" in line]
print(f"Found {len(errors)} errors")
for error in errors[-10:]: # Only return last 10 errors
print(error)
```
## Response format
### Programmatic tool call
When code execution calls a tool:
```json
{
"type": "tool_use",
"id": "toolu_abc123",
"name": "query_database",
"input": { "sql": "" },
"caller": {
"type": "code_execution_20260120",
"tool_id": "srvtoolu_xyz789"
}
}
```
### Tool result handling
Your tool result is passed back to the running code:
```json
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": "toolu_abc123",
"content": "[{\"customer_id\": \"C1\", \"revenue\": 45000, \"orders\": 23}, {\"customer_id\": \"C2\", \"revenue\": 38000, \"orders\": 18}, ...]"
}
]
}
```
### Code execution completion
When all tool calls are satisfied and code completes:
```json
{
"type": "code_execution_tool_result",
"tool_use_id": "srvtoolu_xyz789",
"content": {
"type": "code_execution_result",
"stdout": "Analysis complete. Top 5 customers identified from 847 total records.",
"stderr": "",
"return_code": 0,
"content": []
}
}
```
## Error handling
### Common errors
| Error | Where it appears | Description | Solution |
| ------------------------------------------ | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------- |
| `invalid_tool_input` | `error_code` on the `code_execution_tool_result` error block in the response | Invalid parameters were passed to the code execution tool | See the [code execution tool errors](https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool#errors) |
| `invalid_request_error` (on `tool_choice`) | HTTP 400 error response | `tool_choice` names a tool whose `allowed_callers` does not include `"direct"` | Either add `"direct"` to that tool's `allowed_callers`, or remove the tool from `tool_choice` and let Claude invoke it from code |
### Container expiration during tool call
If your tool result doesn't arrive within about 4 minutes, the pending call raises a `TimeoutError` inside Claude's running code. Claude sees the error in `stderr` and typically retries the call:
```json
{
"type": "code_execution_tool_result",
"tool_use_id": "srvtoolu_abc123",
"content": {
"type": "code_execution_result",
"stdout": "",
"stderr": "TimeoutError: Calling tool ['query_database'] timed out (no response after 270s).",
"return_code": 0,
"content": []
}
}
```
To prevent timeouts:
* Monitor the `expires_at` field in responses
* Implement timeouts for your tool execution
* Consider breaking long operations into smaller chunks
### Tool execution errors
If your tool returns an error:
```json
{
"type": "tool_result",
"tool_use_id": "toolu_abc123",
"content": "Error: Query timeout - table lock exceeded 30 seconds"
}
```
Claude's code receives this error and can handle it appropriately.
## Constraints and limitations
### Feature incompatibilities
* **Structured outputs:** Tools with `strict: true` are not supported with programmatic calling
* **Tool choice:** You cannot force programmatic calling of a specific tool through `tool_choice`
* **Parallel tool use:** `disable_parallel_tool_use: true` is not supported with programmatic calling
### Input schema limitations
Custom tools whose `input_schema` contains a recursive `$ref` (a reference cycle, such as a schema that refers to itself) cannot be enabled for programmatic calling. Including a code execution tool version in `allowed_callers` for such a tool causes the request to fail with a `400 invalid_request_error` whose message contains `Circular $ref detected`. The same schema is accepted for direct tool calling.
To work around this, do one of the following:
* Keep the tool direct-only by omitting `allowed_callers` (or setting it to `["direct"]`). Other tools in the same request can still use programmatic calling.
* Remove the cycle from the schema. For example, unroll the recursion to a fixed depth and describe any deeper nesting in the `description` of the innermost level, or replace the recursive property with a plain `{"type": "object"}` whose `description` explains the expected shape.
### Tool restrictions
The following tools cannot be called programmatically:
* Tools provided by an [MCP connector](https://platform.claude.com/docs/en/agents-and-tools/mcp-connector)
* The [computer use](https://platform.claude.com/docs/en/agents-and-tools/tool-use/computer-use-tool) and [browser use](https://platform.claude.com/docs/en/agents-and-tools/tool-use/browser-use-tool) toolsets (`computer_toolset_20260801` and `browser_toolset_20260801`), whose `allowed_callers` field accepts only `"direct"`
### Message formatting restrictions
When responding to programmatic tool calls, there are strict formatting requirements:
**Tool result only responses:** If there are pending programmatic tool calls waiting for results, your response message must contain **only** `tool_result` blocks. You cannot include any text content, even after the tool results.
Invalid - Cannot include text when responding to programmatic tool calls:
```json
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": "toolu_01",
"content": "[{\"customer_id\": \"C1\", \"revenue\": 45000}]"
},
{ "type": "text", "text": "What should I do next?" }
]
}
```
Valid - Only tool results when responding to programmatic tool calls:
```json
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": "toolu_01",
"content": "[{\"customer_id\": \"C1\", \"revenue\": 45000}]"
}
]
}
```
This restriction only applies when responding to programmatic (code execution) tool calls. For regular client-side tool calls, you can include text content after tool results.
**Text-only tool result content:** The `content` of each `tool_result` that answers a programmatic call must be a string or `text` blocks. Image, document, and other content block types are rejected.
### Rate limits
Programmatic tool calls are subject to the same rate limits as regular tool calls. Each tool call from code execution counts as a separate invocation.
### Validate tool results before use
When implementing user-defined tools that will be called programmatically:
* **Tool results are returned as strings:** They can contain any content, including code snippets or executable commands that may be processed by the execution environment.
* **Validate external tool results:** If your tool returns data from external sources or accepts user input, be aware of code injection risks if the output will be interpreted or executed as code.
## Token efficiency
Programmatic tool calling reduces token consumption in three ways:
* **Tool results from programmatic calls are not added to Claude's context** - only the final code output is
* **Intermediate processing happens in code** - filtering, aggregation, and other transformations don't consume model tokens
* **Multiple tool calls in one code execution** - reduces overhead compared to separate model turns
For example, calling 10 tools directly uses \~10x the tokens of calling them programmatically and returning a summary.
In Anthropic's internal evaluations on a production Claude model:
* On a 75-tool project-management agent benchmark, enabling programmatic tool calling reduced billed input tokens by roughly 38% with no change in task accuracy.
* On [τ²-bench](https://arxiv.org/abs/2506.07982) (airline, retail, and telecom domains), where each turn makes one or two sequential tool calls, programmatic tool calling left scores unchanged and cost roughly 8% more. Sequential single-call workflows do not benefit.
* Across production API traffic, requests whose `tools` array contains 10 to 49 tool definitions see typical token savings of 20% to 40% with programmatic tool calling enabled.
Actual savings vary with workload shape. See [When to use programmatic calling](https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling#when-to-use-programmatic-calling).
## Usage and pricing
Programmatic tool calling uses the same pricing as code execution. See the [code execution pricing](https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool#usage-and-pricing) for details.
Token counting for programmatic tool calls: Tool results from programmatic invocations do not count toward your input/output token usage. Only the final code execution result and Claude's response count.
## Best practices
### Tool design
* **Provide detailed output descriptions:** Because Claude deserializes tool results in code, document the format (JSON structure and field types)
* **Return structured data:** JSON or other machine-readable formats work best for programmatic processing
* **Keep responses concise:** Return only necessary data to minimize processing overhead
### When to use programmatic calling
Programmatic tool calling trades a small fixed overhead (container startup, script generation) for large savings on tool-result tokens and model round-trips. Whether that trade pays off depends on workload shape.
**Strong fit:**
* Fan-out or parallel operations across many items (for example, checking 50 endpoints or looking up 20 records)
* Large tool results that can be filtered, aggregated, or summarized before reaching Claude's context
* Agentic search and retrieval, where iterative querying and result filtering dominate the workflow
**Weak fit:**
* Strictly sequential workflows where each call depends on Claude reasoning over the previous result, because the script cannot skip the model round-trip in that case
* A small number of tool calls with small responses, especially on the first turn of a conversation, where container and script overhead can exceed the savings
* Tools that require immediate user feedback between calls
If you are unsure, measure billed input tokens with and without `allowed_callers` on a representative sample of your traffic before enabling it broadly.
### Performance optimization
* **Reuse containers** when making multiple related requests to maintain state
* **Batch similar operations** in a single code execution when possible
## Troubleshooting
### Common issues
**`invalid_request_error` when setting `tool_choice`**
* `tool_choice` cannot name a tool whose `allowed_callers` omits `"direct"`. Either add `"direct"` to that tool's `allowed_callers`, or remove the tool from `tool_choice` and let Claude invoke it from code.
**Container expiration**
* Respond to each programmatic tool call well before the paused response's `expires_at` timestamp. Claude's code stops waiting for a result after about 4 minutes, and idle containers are currently reclaimed after about 5 minutes.
* Consider implementing faster tool execution
**Tool result not parsed correctly**
* Ensure your tool returns string data that Claude can deserialize
* Provide clear output format documentation in your tool description
### Debugging tips
1. **Log all tool calls and results** to track the flow
2. **Check the `caller` field** to confirm programmatic invocation
3. **Monitor container IDs** to ensure proper reuse
4. **Test tools independently** before enabling programmatic calling
## Why programmatic tool calling works
Claude is trained on large amounts of code, so presenting tools as callable Python functions lets it use that strength:
* **Tool composition:** Chained calls, loops, and conditionals are ordinary Python control flow instead of a series of model round trips
* **Result processing:** Claude's code filters and aggregates large tool outputs, or writes them to files, and only the final output enters the context window
* **Latency:** The model is not re-sampled between the tool calls inside one code execution
## Alternative implementations
Programmatic tool calling is a generalizable pattern that can also be implemented on your own infrastructure. Here's how the approaches compare:
### Client-side direct execution
Provide Claude with a code execution tool and describe what functions are available in that environment. When Claude invokes the tool with code, your application executes it locally where those functions are defined.
**Advantages:**
* Minimal re-architecting of your application
* Full control over the environment and instructions
**Disadvantages:**
* Executes untrusted code outside of a sandbox
* Tool invocations can be vectors for code injection
**Use when:** Your application can safely execute arbitrary code, you want the smallest implementation, and Anthropic's managed offering doesn't fit your needs.
### Self-managed sandboxed execution
Same approach from Claude's perspective, but code runs in a sandboxed container with security restrictions (for example, no network egress). If your tools require external resources, you'll need a protocol for executing tool calls outside the sandbox.
**Advantages:**
* Safe programmatic tool calling on your own infrastructure
* Full control over the execution environment
**Disadvantages:**
* Complex to build and maintain
* Requires managing both infrastructure and inter-process communication
**Use when:** Security is critical and Anthropic's managed solution doesn't fit your requirements.
### Anthropic-managed execution
Anthropic's programmatic tool calling is a managed version of sandboxed execution with an opinionated Python environment tuned for Claude. Anthropic handles container management, code execution, and secure tool invocation communication.
**Advantages:**
* Safe and secure by default
* Enabled with a tool definition, with no infrastructure to run
* Environment and instructions optimized for Claude
Consider using Anthropic's managed solution if you're using the Claude API, [Claude Platform on AWS](https://platform.claude.com/docs/en/build-with-claude/claude-platform-on-aws), or [Microsoft Foundry](https://platform.claude.com/docs/en/build-with-claude/claude-in-microsoft-foundry). On Microsoft Foundry, programmatic tool calling requires a [Hosted on Anthropic deployment](https://platform.claude.com/docs/en/build-with-claude/claude-in-microsoft-foundry#additional-features-not-supported-when-hosted-on-azure).
## Data retention
Programmatic tool calling is built on the code execution infrastructure and uses the same sandbox containers. Container data, including execution artifacts and outputs, is retained for up to 30 days.
For ZDR eligibility across all features, see [API and data retention](https://platform.claude.com/docs/en/manage-claude/api-and-data-retention).
## Next steps
Stream tool inputs without server-side JSON buffering for latency-sensitive applications.
Run Python and bash code in a sandboxed container to analyze data, generate files, and iterate on solutions.
Connect Claude to external tools and APIs. See where tools execute, when Claude calls them, and which tool fits your task.
Specify tool schemas, write effective descriptions, and control when Claude calls your tools.