@AiTool -- Framework-Agnostic Tool Calling

In Chapter 9 you built an AI endpoint that streams LLM responses to the browser. But LLMs can do more than generate text — they can decide to call tools: functions you define that the model can invoke when it needs external data or wants to take an action. This chapter covers @AiTool, Atmosphere’s framework-agnostic annotation for tool calling.

The Problem with Framework-Specific Tools

Every AI framework has its own way of defining tools:

• LangChain4j uses @Tool on methods with @P for parameters
• Spring AI uses ToolCallback and ToolDefinition interfaces
• Google ADK uses BaseTool classes

If you define tools with one framework’s annotations, switching to another requires rewriting every tool. Atmosphere solves this with @AiTool — you define tools once, and the framework bridges them to whatever backend you are using.

@AiTool Annotation

@AiTool marks a method as an AI-callable tool. It has two required attributes:

@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
public @interface AiTool {

    /** Tool name as exposed to the AI model. Convention: snake_case. */
    String name();

    /** Human-readable description of what the tool does. */
    String description();
}

Attribute	Type	Description
name	String	Unique tool name (snake_case convention, e.g., "get_weather")
description	String	Human-readable description sent to the model to help it decide when to call the tool

@Param Annotation

@Param annotates parameters of an @AiTool-annotated method to provide metadata for the AI model’s tool schema:

@Target(ElementType.PARAMETER)
@Retention(RetentionPolicy.RUNTIME)
public @interface Param {

    /** Parameter name as exposed to the AI model. */
    String value();

    /** Human-readable description of this parameter. */
    String description() default "";

    /** Whether this parameter is required. Defaults to true. */
    boolean required() default true;
}

Attribute	Type	Default	Description
value	String	(required)	Parameter name as exposed to the model
description	String	""	Human-readable description
required	boolean	true	Whether the model must provide this parameter

Complete Example: AssistantTools

This is the AssistantTools class from the spring-boot-ai-tools sample:

public class AssistantTools {

    @AiTool(name = "get_current_time",
            description = "Returns the current date and time in the server's timezone")
    public String getCurrentTime() {
        return ZonedDateTime.now()
                .format(DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss z"));
    }

    @AiTool(name = "get_city_time",
            description = "Returns the current time in a specific city")
    public String getCityTime(
            @Param(value = "city",
                   description = "City name (e.g., Tokyo, London, Paris, New York, Sydney)")
            String city) {
        var zone = switch (city.toLowerCase()) {
            case "tokyo" -> "Asia/Tokyo";
            case "london" -> "Europe/London";
            case "paris" -> "Europe/Paris";
            case "sydney" -> "Australia/Sydney";
            case "new york", "nyc" -> "America/New_York";
            case "los angeles", "la" -> "America/Los_Angeles";
            default -> "UTC";
        };
        return city + ": " + ZonedDateTime.now(ZoneId.of(zone))
                .format(DateTimeFormatter.ofPattern("HH:mm:ss (z)"));
    }

    @AiTool(name = "get_weather",
            description = "Returns a weather report for a city with temperature and conditions")
    public String getWeather(
            @Param(value = "city", description = "City name to get weather for")
            String city) {
        return switch (city.toLowerCase()) {
            case "london" -> "London: Cloudy, 15C / 59F, 80% humidity";
            case "paris" -> "Paris: Partly cloudy, 20C / 68F, 65% humidity";
            case "tokyo" -> "Tokyo: Rainy, 22C / 72F, 90% humidity";
            default -> city + ": Clear, 22C / 72F, 50% humidity";
        };
    }

    @AiTool(name = "convert_temperature",
            description = "Converts a temperature between Celsius and Fahrenheit")
    public String convertTemperature(
            @Param(value = "value", description = "The temperature value to convert")
            double value,
            @Param(value = "from_unit",
                   description = "Source unit: 'C' for Celsius or 'F' for Fahrenheit")
            String fromUnit) {
        if ("C".equalsIgnoreCase(fromUnit) || "celsius".equalsIgnoreCase(fromUnit)) {
            double fahrenheit = value * 9.0 / 5.0 + 32;
            return String.format("%.1fC = %.1fF", value, fahrenheit);
        } else {
            double celsius = (value - 32) * 5.0 / 9.0;
            return String.format("%.1fF = %.1fC", value, celsius);
        }
    }
}

Key observations:

• No framework imports — the class uses only org.atmosphere.ai.annotation.* and standard JDK types.
• Return types are plain Java — String, not framework-specific result objects.
• Parameters use @Param — providing name, description, and optionally required = false.
• Type inference — the double parameter for convert_temperature is automatically mapped to JSON Schema type "number" by the ToolParameter.jsonSchemaType() method.

Connecting Tools to an @AiEndpoint

Use the tools attribute on @AiEndpoint:

@AiEndpoint(path = "/atmosphere/langchain4j-tools/{room}",
        systemPromptResource = "prompts/system-prompt.md",
        conversationMemory = true,
        maxHistoryMessages = 30,
        tools = AssistantTools.class,
        interceptors = CostMeteringInterceptor.class)
public class AiToolsChat {

    @PathParam("room")
    private String room;

    @Ready
    public void onReady(AtmosphereResource resource) {
        logger.info("[room={}] Client {} connected (peers: {})",
                room, resource.uuid(),
                resource.getBroadcaster().getAtmosphereResources().size());
    }

    @Disconnect
    public void onDisconnect(AtmosphereResourceEvent event) {
        logger.info("[room={}] Client {} disconnected",
                room, event.getResource().uuid());
    }

    @Prompt
    public void onPrompt(String message, StreamingSession session,
                          AtmosphereResource resource) {
        logger.info("[room={}] Prompt from {}: {}", room, resource.uuid(), message);

        var settings = AiConfig.get();
        if (settings == null || settings.client().apiKey() == null
                || settings.client().apiKey().isBlank()) {
            DemoResponseProducer.stream(message, session, room, "unknown");
            return;
        }

        session.stream(message);
    }
}

This endpoint demonstrates the full AI tool pipeline:

1. tools = AssistantTools.class — tells the framework to scan AssistantTools for @AiTool-annotated methods and register them.
2. conversationMemory = true — enables multi-turn context so the model can reference previous tool results.
3. maxHistoryMessages = 30 — retains up to 30 messages (15 turns) of conversation history.
4. interceptors = CostMeteringInterceptor.class — adds cost estimation and routing metadata.
5. @PathParam("room") — URI template variable for per-room AI sessions.

When session.stream(message) is called:

1. Tools from AssistantTools are attached to the AiRequest
2. The framework bridges them to the active backend’s native tool format
3. The backend handles the tool call loop automatically
4. Tool results are fed back to the model for the final response

Multiple Tool Classes

You can specify multiple tool provider classes:

@AiEndpoint(path = "/chat",
    tools = {WeatherTools.class, CalendarTools.class, MathTools.class})

Excluding Tools

When tools is empty (the default), all globally registered tools are available. Use excludeTools to selectively remove some:

@AiEndpoint(path = "/public-chat",
    excludeTools = {AdminTools.class})

ToolRegistry

The ToolRegistry is the global registry where tool definitions are stored. Tools are registered at startup (via @AiTool scanning or manual registration) and selected per-endpoint.

public interface ToolRegistry {

    void register(ToolDefinition tool);

    void register(Object toolProvider);

    Optional<ToolDefinition> getTool(String name);

    Collection<ToolDefinition> getTools(Collection<String> names);

    Collection<ToolDefinition> allTools();

    boolean unregister(String name);

    ToolResult execute(String toolName, Map<String, Object> arguments);
}

Method	Description
register(ToolDefinition)	Register a single tool definition
register(Object)	Scan an object for @AiTool-annotated methods and register all of them
getTool(name)	Look up a tool by name
getTools(names)	Get tools matching the given names (silently skips unknown names)
allTools()	Get all registered tools
unregister(name)	Remove a tool by name
execute(toolName, arguments)	Execute a tool with the given arguments

ToolDefinition Record

Each registered tool is represented as a ToolDefinition record:

public record ToolDefinition(
        String name,
        String description,
        List<ToolParameter> parameters,
        String returnType,
        ToolExecutor executor
) { }

Field	Type	Description
name	String	Unique tool name (must not be blank)
description	String	Description for the model (must not be blank)
parameters	List<ToolParameter>	Ordered parameter definitions
returnType	String	JSON Schema type of the return value (default: "string")
executor	ToolExecutor	The function that executes the tool

ToolParameter Record

public record ToolParameter(
        String name,
        String description,
        String type,       // JSON Schema type: string, integer, number, boolean, object, array
        boolean required
) { }

The ToolParameter.jsonSchemaType(Class<?>) static method maps Java types to JSON Schema types:

Java Type	JSON Schema Type
String, CharSequence	"string"
int, Integer, long, Long	"integer"
float, Float, double, Double	"number"
boolean, Boolean	"boolean"
Everything else	"object"

ToolExecutor Interface

@FunctionalInterface
public interface ToolExecutor {
    Object execute(Map<String, Object> arguments) throws Exception;
}

The executor receives arguments as a Map<String, Object> keyed by parameter name and returns a result that will be serialized to JSON and sent back to the model.

ToolResult Record

public record ToolResult(String toolName, String result, boolean success, String error) {
    public static ToolResult success(String toolName, String result) { ... }
    public static ToolResult failure(String toolName, String error) { ... }
}

Manual Tool Registration

You can register tools programmatically without annotations using the builder:

var tool = ToolDefinition.builder("calculate_area", "Calculate the area of a rectangle")
        .parameter("width", "Width in meters", "number")
        .parameter("height", "Height in meters", "number")
        .returnType("number")
        .executor(args -> {
            double w = ((Number) args.get("width")).doubleValue();
            double h = ((Number) args.get("height")).doubleValue();
            return w * h;
        })
        .build();

toolRegistry.register(tool);

The builder API:

Builder Method	Description
builder(name, description)	Create a new builder
parameter(name, description, type)	Add a required parameter
parameter(name, description, type, required)	Add a parameter with explicit required flag
returnType(type)	Set the return type (default: "string")
executor(ToolExecutor)	Set the execution function (required)
build()	Build the ToolDefinition

How Tool Bridging Works

When session.stream(message) is called on an endpoint with tools, the framework:

1. Collects all ToolDefinition instances from the ToolRegistry that match the endpoint’s tools attribute
2. Bridges them to the active backend’s native format using a ToolBridge
3. The backend handles the tool call loop

Each AI backend has its own bridge:

Spring AI (SpringAiToolBridge)

Converts Atmosphere ToolDefinition to Spring AI ToolCallback:

• Builds a JSON Schema from ToolParameter definitions
• Wraps the ToolExecutor in a ToolCallback.call(String) implementation
• Spring AI handles the tool call loop automatically — it invokes the callback and feeds results back to the model

LangChain4j (LangChain4jToolBridge)

Converts Atmosphere ToolDefinition to LangChain4j ToolSpecification:

• Maps ToolParameter types to LangChain4j JSON schema elements (JsonStringSchema, JsonIntegerSchema, JsonNumberSchema, JsonBooleanSchema)
• Unlike Spring AI, LangChain4j does not automatically execute tool callbacks — when the model responds with ToolExecutionRequests, the LangChain4jToolBridge.executeToolCalls() method runs the tools and returns ToolExecutionResultMessages to feed back to the model
• This loop is handled by ToolAwareStreamingResponseHandler

Google ADK (AdkToolBridge)

Converts Atmosphere ToolDefinition to ADK BaseTool:

• Wraps each tool as an ADK-compatible tool object
• ADK handles the tool call loop through its agent event system

Conversation Memory with Tools

When tools are combined with conversationMemory = true, the conversation history includes tool calls and results. This lets the model:

1. Reference previous tool results (“What was the weather in London earlier?”)
2. Build on previous answers (“Convert that temperature to Fahrenheit”)
3. Use context from earlier turns to decide whether to call a tool again

The AiConversationMemory stores ChatMessage objects, and the sliding window at maxHistoryMessages ensures memory usage stays bounded.

@AiEndpoint(path = "/chat",
    tools = AssistantTools.class,
    conversationMemory = true,
    maxHistoryMessages = 30)

With 30 messages and 4 tools, a typical conversation might look like:

User: "What time is it in Tokyo?"
→ Tool call: get_city_time(city="Tokyo")
→ Tool result: "Tokyo: 14:23:45 (JST)"
→ Assistant: "It's currently 2:23 PM in Tokyo (JST)."
User: "And the weather there?"
→ Tool call: get_weather(city="Tokyo")
→ Tool result: "Tokyo: Rainy, 22C / 72F, 90% humidity"
→ Assistant: "Tokyo is currently rainy at 22C..."

All of these messages are retained in the conversation memory, giving the model full context for follow-up questions.

Samples

Two sample applications demonstrate tool calling:

• samples/spring-boot-ai-tools/ — uses the built-in LLM client with @AiTool methods (AssistantTools), conversation memory, and the CostMeteringInterceptor. Run with: ./mvnw spring-boot:run -pl samples/spring-boot-ai-tools
• samples/spring-boot-langchain4j-tools/ — same tools, but powered by the LangChain4j adapter with ToolAwareStreamingResponseHandler. Run with: ./mvnw spring-boot:run -pl samples/spring-boot-langchain4j-tools

Both samples share the same AssistantTools class, demonstrating that @AiTool definitions are adapter-independent.

Summary

Concept	Purpose
@AiTool(name, description)	Marks a method as an AI-callable tool
@Param(value, description, required)	Provides parameter metadata for the tool schema
ToolRegistry	Global registry for tool definitions
ToolDefinition	Record: name, description, parameters, returnType, executor
ToolParameter	Record: name, description, JSON Schema type, required
ToolExecutor	Functional interface that executes the tool
ToolResult	Record: toolName, result, success, error
SpringAiToolBridge	Bridges to Spring AI ToolCallback
LangChain4jToolBridge	Bridges to LangChain4j ToolSpecification
AdkToolBridge	Bridges to Google ADK BaseTool
@AiEndpoint(tools={...})	Selects which tool classes are available at this endpoint
conversationMemory = true	Enables multi-turn history including tool calls and results

In the next chapter, you will learn how Atmosphere’s AI adapters connect to Spring AI, LangChain4j, Google ADK, and the built-in OpenAI-compatible client.