# Function tools

<div class="language-support-tag">
  <span class="lst-supported">Supported in ADK</span><span class="lst-python">Python v0.1.0</span><span class="lst-typescript">Typescript v0.2.0</span><span class="lst-go">Go v0.1.0</span><span class="lst-java">Java v0.1.0</span>
</div>

When pre-built ADK tools don't meet your requirements, you can create custom *function tools*. Building function tools allows you to create tailored functionality, such as connecting to proprietary databases or implementing unique algorithms.
For example, a function tool, `myfinancetool`, might be a function that calculates a specific financial metric. ADK also supports long-running functions, so if that calculation takes a while, the agent can continue working on other tasks.

ADK offers several ways to create functions tools, each suited to different levels of complexity and control:

*  [Function Tools](#function-tool)
*  [Long Running Function Tools](#long-run-tool)
*  [Agents-as-a-Tool](#agent-tool)

## Function Tools {#function-tool}

Transforming a Python function into a tool is a straightforward way to integrate custom logic into your agents. When you assign a function to an agent’s `tools` list, the framework automatically wraps it as a `FunctionTool`.

### How it Works

The ADK framework automatically inspects your Python function's signature—including its name, docstring, parameters, type hints, and default values—to generate a schema. This schema is what the LLM uses to understand the tool's purpose, when to use it, and what arguments it requires.

### Defining Function Signatures

A well-defined function signature is crucial for the LLM to use your tool correctly.

#### Parameters

##### Required Parameters

=== "Python"
    A parameter is considered **required** if it has a type hint but **no default value**. The LLM must provide a value for this argument when it calls the tool. The parameter's description is taken from the function's docstring.

    ???+ "Example: Required Parameters"
        ```python
        def get_weather(city: str, unit: str):
            """
            Retrieves the weather for a city in the specified unit.

            Args:
                city (str): The city name.
                unit (str): The temperature unit, either 'Celsius' or 'Fahrenheit'.
            """
            # ... function logic ...
            return {"status": "success", "report": f"Weather for {city} is sunny."}
        ```
    In this example, both `city` and `unit` are mandatory. If the LLM tries to call `get_weather` without one of them, the ADK will return an error to the LLM, prompting it to correct the call.

=== "Go"
    In Go, you use struct tags to control the JSON schema. The two primary tags are `json` and `jsonschema`.

    A parameter is considered **required** if its struct field does **not** have the `omitempty` or `omitzero` option in its `json` tag.

    The `jsonschema` tag is used to provide the argument's description. This is crucial for the LLM to understand what the argument is for.

    ???+ "Example: Required Parameters"
        ```go
        // GetWeatherParams defines the arguments for the getWeather tool.
        type GetWeatherParams struct {
            // This field is REQUIRED (no "omitempty").
            // The jsonschema tag provides the description.
            Location string `json:"location" jsonschema:"The city and state, e.g., San Francisco, CA"`

            // This field is also REQUIRED.
            Unit     string `json:"unit" jsonschema:"The temperature unit, either 'celsius' or 'fahrenheit'"`
        }
        ```
    In this example, both `location` and `unit` are mandatory.

=== "Java"
    In Java, primitive types (e.g., `int`, `double`, `boolean`) are inherently **required** because they cannot be null. For object types (like `String` or `Integer`), they are typically considered required unless explicitly marked as optional.

    The `@Schema` annotation is used to provide the argument's description and can explicitly define parameter properties. This is crucial for the LLM to understand what the argument is for.

    ???+ "Example: Required Parameters"
        ```java
        // The @Schema annotation on the parameter provides the description.
        public static Map<String, Object> getWeather(
            @Schema(description = "The city and state, e.g., San Francisco, CA", name = "location")
            String location,

            @Schema(description = "The temperature unit, either 'Celsius' or 'Fahrenheit'", name = "unit")
            String unit) {

            // ... function logic ...
            return Map.of("status", "success", "report", "Weather for " + location + " is sunny.");
        }
        ```
    In this example, both `location` and `unit` are mandatory.

##### Optional Parameters

=== "Python"
    A parameter is considered **optional** if you provide a **default value**. This is the standard Python way to define optional arguments. You can also mark a parameter as optional using `typing.Optional[SomeType]` or the `| None` syntax (Python 3.10+).

    ???+ "Example: Optional Parameters"
        ```python
        def search_flights(destination: str, departure_date: str, flexible_days: int = 0):
            """
            Searches for flights.

            Args:
                destination (str): The destination city.
                departure_date (str): The desired departure date.
                flexible_days (int, optional): Number of flexible days for the search. Defaults to 0.
            """
            # ... function logic ...
            if flexible_days > 0:
                return {"status": "success", "report": f"Found flexible flights to {destination}."}
            return {"status": "success", "report": f"Found flights to {destination} on {departure_date}."}
        ```
    Here, `flexible_days` is optional. The LLM can choose to provide it, but it's not required.

=== "Go"
    A parameter is considered **optional** if its struct field has the `omitempty` or `omitzero` option in its `json` tag.

    ???+ "Example: Optional Parameters"
        ```go
        // GetWeatherParams defines the arguments for the getWeather tool.
        type GetWeatherParams struct {
            // Location is required.
            Location string `json:"location" jsonschema:"The city and state, e.g., San Francisco, CA"`

            // Unit is optional.
            Unit string `json:"unit,omitempty" jsonschema:"The temperature unit, either 'celsius' or 'fahrenheit'"`

            // Days is optional.
            Days int `json:"days,omitzero" jsonschema:"The number of forecast days to return (defaults to 1)"`
        }
        ```
    Here, `unit` and `days` are optional. The LLM can choose to provide them, but they are not required.

=== "Java"
    A parameter can be considered **optional** in Java by using object types that allow `null` values (such as `Integer` instead of `int`), or by explicitly defining it as optional using `java.util.Optional`.

    ???+ "Example: Optional Parameters"
        ```java
        import java.util.Map;
        import java.util.Optional;

        public static Map<String, Object> searchFlights(
            @Schema(description = "The destination city.", name = "destination")
            String destination,

            @Schema(description = "The desired departure date.", name = "departureDate")
            String departureDate,

            @Schema(description = "Number of flexible days for the search. Defaults to 0.", name = "flexibleDays")
            Optional<Integer> flexibleDays) {

            // ... function logic ...
            int days = flexibleDays.orElse(0);
            if (days > 0) {
                return Map.of("status", "success", "report", "Found flexible flights to " + destination + ".");
            }
            return Map.of("status", "success", "report", "Found flights to " + destination + " on " + departureDate + ".");
        }
        ```
    Here, `flexibleDays` is optional. The LLM can choose to provide it, but it's not required.

##### Optional Parameters with `typing.Optional`
You can also mark a parameter as optional using `typing.Optional[SomeType]` or the `| None` syntax (Python 3.10+). This signals that the parameter can be `None`. When combined with a default value of `None`, it behaves as a standard optional parameter.

???+ "Example: `typing.Optional`"
    === "Python"
        ```python
        from typing import Optional

        def create_user_profile(username: str, bio: Optional[str] = None):
            """
            Creates a new user profile.

            Args:
                username (str): The user's unique username.
                bio (str, optional): A short biography for the user. Defaults to None.
            """
            # ... function logic ...
            if bio:
                return {"status": "success", "message": f"Profile for {username} created with a bio."}
            return {"status": "success", "message": f"Profile for {username} created."}
        ```

##### Variadic Parameters (`*args` and `**kwargs`)
While you can include `*args` (variable positional arguments) and `**kwargs` (variable keyword arguments) in your function signature for other purposes, they are **ignored by the ADK framework** when generating the tool schema for the LLM. The LLM will not be aware of them and cannot pass arguments to them. It's best to rely on explicitly defined parameters for all data you expect from the LLM.

#### Return Type

The preferred return type for a Function Tool is a **dictionary** in Python, a **Map** or custom **Record or POJO** in Java, or an **object** in TypeScript. This allows you to structure the response with key-value pairs, providing context and clarity to the LLM. If your function returns a type other than a dictionary or map, the framework automatically wraps it into a dictionary with a single key named **"result"**.

Strive to make your return values as descriptive as possible. *For example,* instead of returning a numeric error code, return a dictionary with an "error_message" key containing a human-readable explanation. **Remember that the LLM**, not a piece of code, needs to understand the result. As a best practice, include a "status" key in your return dictionary to indicate the overall outcome (e.g., "success", "error", "pending"), providing the LLM with a clear signal about the operation's state.

#### Docstrings

The docstring of your function serves as the tool's **description** and is sent to the LLM. Therefore, a well-written and comprehensive docstring is crucial for the LLM to understand how to use the tool effectively. Clearly explain the purpose of the function, the meaning of its parameters, and the expected return values. In Java, you can use Javadoc comments or the `@Schema(description="...")` annotation on your method to serve as this description.

### Passing Data Between Tools

When an agent calls multiple tools in a sequence, you might need to pass data from one tool to another. The recommended way to do this is by using the `temp:` prefix in the session state.

A tool can write data to a `temp:` variable, and a subsequent tool can read it. This data is only available for the current invocation and is discarded afterwards.

!!! note "Shared Invocation Context"
    All tool calls within a single agent turn share the same `InvocationContext`. This means they also share the same temporary (`temp:`) state, which is how data can be passed between them.

### Example

??? "Example"

    === "Python"

        This tool is a python function which obtains the Stock price of a given Stock ticker/ symbol.

        <u>Note</u>: You need to `pip install yfinance` library before using this tool.

        ```python
        --8<-- "examples/python/snippets/tools/function-tools/func_tool.py"
        ```

        The return value from this tool will be wrapped into a dictionary.

        ```json
        {"result": "$123"}
        ```

    === "Typescript"

        This tool retrieves the mocked value of a stock price.

        ```typescript
        --8<-- "examples/typescript/snippets/tools/function-tools/function-tools-example.ts"
        ```

        The return value from this tool will be an object.

        ```json
        For input `GOOG`: {"price": 2800.0, "currency": "USD"}
        ```

    === "Go"

        This tool retrieves the mocked value of a stock price.

        ```go
        import (
            "google.golang.org/adk/agent"
            "google.golang.org/adk/agent/llmagent"
            "google.golang.org/adk/model/gemini"
            "google.golang.org/adk/runner"
            "google.golang.org/adk/session"
            "google.golang.org/adk/tool"
            "google.golang.org/adk/tool/functiontool"
            "google.golang.org/genai"
        )

        --8<-- "examples/go/snippets/tools/function-tools/func_tool.go"
        ```

        The return value from this tool will be a `getStockPriceResults` instance.

        ```json
        For input `{"symbol": "GOOG"}`: {"price":300.6,"symbol":"GOOG"}
        ```

    === "Java"

        This tool retrieves the mocked value of a stock price.

        ```java
        --8<-- "examples/java/snippets/src/main/java/tools/StockPriceAgent.java:full_code"
        ```

        The return value from this tool will be wrapped into a Map<String, Object>.

        ```json
        For input `GOOG`: {"symbol": "GOOG", "price": "1.0"}
        ```

### Best Practices

While you have considerable flexibility in defining your function, remember that simplicity enhances usability for the LLM. Consider these guidelines:

* **Fewer Parameters are Better:** Minimize the number of parameters to reduce complexity.
* **Simple Data Types:** Favor primitive data types like `str` and `int` over custom classes whenever possible.
* **Meaningful Names:** The function's name and parameter names significantly influence how the LLM interprets and utilizes the tool. Choose names that clearly reflect the function's purpose and the meaning of its inputs. Avoid generic names like `do_stuff()` or `beAgent()`.
* **Build for Parallel Execution:** Improve function calling performance when multiple tools are run by building for asynchronous operation. For information on enabling parallel execution for tools, see
[Increase tool performance with parallel execution](/tools-custom/performance/).

## Long Running Function Tools {#long-run-tool}

This tool is designed to help you start and manage tasks that are handled outside the operation of your agent workflow, and require a significant amount of processing time, without blocking the agent's execution. This tool is a subclass of `FunctionTool`.

When using a `LongRunningFunctionTool`, your function can initiate the long-running operation and optionally return an **initial result**, such as a long-running operation id. Once a long running function tool is invoked the agent runner pauses the agent run and lets the agent client to decide whether to continue or wait until the long-running operation finishes. The agent client can query the progress of the long-running operation and send back an intermediate or final response. The agent can then continue with other tasks. An example is the human-in-the-loop scenario where the agent needs human approval before proceeding with a task.

!!! warning "Warning: Execution handling"
    Long Running Function Tools are designed to help you start and *manage* long running
    tasks as part of your agent workflow, but ***not perform*** the actual, long task.
    For tasks that require significant time to complete, you should implement a separate
    server to do the task.

!!! tip "Tip: Parallel execution"
    Depending on the type of tool you are building, designing for asynchronous
    operation may be a better solution than creating a long running tool. For
    more information, see
    [Increase tool performance with parallel execution](/tools-custom/performance/).

### How it Works

In Python, you wrap a function with `LongRunningFunctionTool`. In Java, you pass a Method name to `LongRunningFunctionTool.create()`. In TypeScript, you instantiate the `LongRunningFunctionTool` class.


1. **Initiation:** When the LLM calls the tool, your function starts the long-running operation.

2. **Initial Updates:** Your function should optionally return an initial result (e.g. the long-running operation id). The ADK framework takes the result and sends it back to the LLM packaged within a `FunctionResponse`. This allows the LLM to inform the user (e.g., status, percentage complete, messages). And then the agent run is ended / paused.

3. **Continue or Wait:** After each agent run is completed. Agent client can query the progress of the long-running operation and decide whether to continue the agent run with an intermediate response (to update the progress) or wait until a final response is retrieved. Agent client should send the intermediate or final response back to the agent for the next run.

4. **Framework Handling:** The ADK framework manages the execution. It sends the intermediate or final `FunctionResponse` sent by agent client to the LLM to generate a user friendly message.

### Creating the Tool

Define your tool function and wrap it using the `LongRunningFunctionTool` class:

=== "Python"

    ```python
    --8<-- "examples/python/snippets/tools/function-tools/human_in_the_loop.py:define_long_running_function"
    ```

=== "TypeScript"

    ```typescript
    --8<-- "examples/typescript/snippets/tools/function-tools/long-running-function-tool-example.ts:define_long_running_function"
    ```

=== "Go"

    ```go
    import (
        "google.golang.org/adk/agent"
        "google.golang.org/adk/agent/llmagent"
        "google.golang.org/adk/model/gemini"
        "google.golang.org/adk/tool"
        "google.golang.org/adk/tool/functiontool"
        "google.golang.org/genai"
    )

    --8<-- "examples/go/snippets/tools/function-tools/long-running-tool/long_running_tool.go:create_long_running_tool"
    ```

=== "Java"

    ```java
    import com.google.adk.agents.LlmAgent;
    import com.google.adk.tools.LongRunningFunctionTool;
    import java.util.HashMap;
    import java.util.Map;

    public class ExampleLongRunningFunction {

      // Define your Long Running function.
      // Ask for approval for the reimbursement.
      public static Map<String, Object> askForApproval(String purpose, double amount) {
        // Simulate creating a ticket and sending a notification
        System.out.println(
            "Simulating ticket creation for purpose: " + purpose + ", amount: " + amount);

        // Send a notification to the approver with the link of the ticket
        Map<String, Object> result = new HashMap<>();
        result.put("status", "pending");
        result.put("approver", "Sean Zhou");
        result.put("purpose", purpose);
        result.put("amount", amount);
        result.put("ticket-id", "approval-ticket-1");
        return result;
      }

      public static void main(String[] args) throws NoSuchMethodException {
        // Pass the method to LongRunningFunctionTool.create
        LongRunningFunctionTool approveTool =
            LongRunningFunctionTool.create(ExampleLongRunningFunction.class, "askForApproval");

        // Include the tool in the agent
        LlmAgent approverAgent =
            LlmAgent.builder()
                // ...
                .tools(approveTool)
                .build();
      }
    }
    ```

### Intermediate / Final result Updates

Agent client received an event with long running function calls and check the status of the ticket. Then Agent client can send the intermediate or final response back to update the progress. The framework packages this value (even if it's None) into the content of the `FunctionResponse` sent back to the LLM.

!!! note "Note: Long running function response with Resume feature"

    If your ADK agent workflow is configured with the
    [Resume](/runtime/resume/) feature, you also must include
    the Invocation ID (`invocation_id`) parameter with the long running
    function response. The Invocation ID you provide must be the same
    invocation that generated the long running function request, otherwise
    the system starts a new invocation with the response. If your
    agent uses the Resume feature, consider including the Invocation ID
    as a parameter with your long running function request, so it can be
    included with the response. For more details on using the Resume
    feature, see
    [Resume stopped agents](/runtime/resume/).

??? Tip "Applies to only Java ADK"

    When passing `ToolContext` with Function Tools, ensure that one of the following is true:

    * The Schema is passed with the ToolContext parameter in the function signature, like:
      ```
      @com.google.adk.tools.Annotations.Schema(name = "toolContext") ToolContext toolContext
      ```
    OR

    * The following `-parameters` flag is set to the mvn compiler plugin

    ```
    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.14.0</version> <!-- or newer -->
                <configuration>
                    <compilerArgs>
                        <arg>-parameters</arg>
                    </compilerArgs>
                </configuration>
            </plugin>
        </plugins>
    </build>
    ```
    This constraint is temporary and will be removed.


=== "Python"

    ```python
    --8<-- "examples/python/snippets/tools/function-tools/human_in_the_loop.py:call_reimbursement_tool"
    ```

=== "TypeScript"

    ```typescript
    --8<-- "examples/typescript/snippets/tools/function-tools/long-running-function-tool-example.ts"
    ```

=== "Go"

    The following example demonstrates a multi-turn workflow. First, the user asks the agent to create a ticket. The agent calls the long-running tool and the client captures the `FunctionCall` ID. The client then simulates the asynchronous work completing by sending subsequent `FunctionResponse` messages back to the agent to provide the ticket ID and final status.

    ```go
    --8<-- "examples/go/snippets/tools/function-tools/long-running-tool/long_running_tool.go:run_long_running_tool"
    ```

=== "Java"

    ```java
    --8<-- "examples/java/snippets/src/main/java/tools/LongRunningFunctionExample.java:full_code"
    ```


??? "Python complete example: File Processing Simulation"

    ```python
    --8<-- "examples/python/snippets/tools/function-tools/human_in_the_loop.py"
    ```

#### Key aspects of this example

* **`LongRunningFunctionTool`**: Wraps the supplied method/function; the framework handles sending yielded updates and the final return value as sequential FunctionResponses.

* **Agent instruction**: Directs the LLM to use the tool and understand the incoming FunctionResponse stream (progress vs. completion) for user updates.

* **Final return**: The function returns the final result dictionary, which is sent in the concluding FunctionResponse to indicate completion.

## Agent-as-a-Tool {#agent-tool}

This powerful feature allows you to leverage the capabilities of other agents within your system by calling them as tools. The Agent-as-a-Tool enables you to invoke another agent to perform a specific task, effectively **delegating responsibility**. This is conceptually similar to creating a Python function that calls another agent and uses the agent's response as the function's return value.

### Key difference from sub-agents

It's important to distinguish an Agent-as-a-Tool from a Sub-Agent.

* **Agent-as-a-Tool:** When Agent A calls Agent B as a tool (using Agent-as-a-Tool), Agent B's answer is **passed back** to Agent A, which then summarizes the answer and generates a response to the user. Agent A retains control and continues to handle future user input.

* **Sub-agent:** When Agent A calls Agent B as a sub-agent, the responsibility of answering the user is completely **transferred to Agent B**. Agent A is effectively out of the loop. All subsequent user input will be answered by Agent B.

### Usage

To use an agent as a tool, wrap the agent with the AgentTool class.

=== "Python"

    ```python
    tools=[AgentTool(agent=agent_b)]
    ```

=== "TypeScript"

    ```typescript
    tools: [new AgentTool({agent: agentB})]
    ```

=== "Go"

    ```go
    agenttool.New(agent, &agenttool.Config{...})
    ```

=== "Java"

    ```java
    AgentTool.create(agent)
    ```

### Customization

The `AgentTool` class provides the following attributes for customizing its behavior:

* **skip\_summarization: bool:** If set to True, the framework will **bypass the LLM-based summarization** of the tool agent's response. This can be useful when the tool's response is already well-formatted and requires no further processing.

??? "Example"

    === "Python"

        ```python
        --8<-- "examples/python/snippets/tools/function-tools/summarizer.py"
        ```

    === "TypeScript"

        ```typescript
        --8<-- "examples/typescript/snippets/tools/function-tools/agent-as-a-tool-example.ts"
        ```

    === "Go"

        ```go
        import (
            "google.golang.org/adk/agent"
            "google.golang.org/adk/agent/llmagent"
            "google.golang.org/adk/model/gemini"
            "google.golang.org/adk/tool"
            "google.golang.org/adk/tool/agenttool"
            "google.golang.org/genai"
        )

        --8<-- "examples/go/snippets/tools/function-tools/func_tool.go:agent_tool_example"
        ```

    === "Java"

        ```java
        --8<-- "examples/java/snippets/src/main/java/tools/AgentToolCustomization.java:full_code"
        ```

### How it works

1. When the `main_agent` receives the long text, its instruction tells it to use the 'summarize' tool for long texts.
2. The framework recognizes 'summarize' as an `AgentTool` that wraps the `summary_agent`.
3. Behind the scenes, the `main_agent` will call the `summary_agent` with the long text as input.
4. The `summary_agent` will process the text according to its instruction and generate a summary.
5. **The response from the `summary_agent` is then passed back to the `main_agent`.**
6. The `main_agent` can then take the summary and formulate its final response to the user (e.g., "Here's a summary of the text: ...")