---
title: "Tool"
id: tool
slug: "/tool"
description: "`Tool` is a data class representing a function that Language Models can prepare a call for."
---

# Tool

`Tool` is a data class representing a function that Language Models can prepare a call for.

A growing number of Language Models now support passing tool definitions alongside the prompt.

Tool calling refers to the ability of Language Models to generate calls to tools - be they functions or APIs - when responding to user queries. The model prepares the tool call but does not execute it.

If you are looking for the details of this data class's methods and parameters, visit our [API documentation](/reference/tools-api).

## Tool class

`Tool` is a simple and unified abstraction to represent tools in the Haystack framework.

A tool is a function for which Language Models can prepare a call.

The `Tool` class is used in Chat Generators and provides a consistent experience across models. `Tool` is also used in the [`ToolInvoker`](../pipeline-components/tools/toolinvoker.mdx) component that executes calls prepared by Language Models.

```python
@dataclass
class Tool:
    name: str
    description: str
    parameters: Dict[str, Any]
    function: Callable
    outputs_to_string: dict[str, Any] | None = None
    inputs_from_state: dict[str, str] | None = None
    outputs_to_state: dict[str, dict[str, Any]] | None = None
```

- `name` is the name of the Tool.
- `description` is a string describing what the Tool does.
- `parameters` is a JSON schema describing the expected parameters.
- `function` is invoked when the Tool is called.
- `outputs_to_string` (optional) controls how parts of the tool’s output are converted into one or more strings (e.g. for LLM consumption).
- `inputs_from_state` (optional) maps values from the agent state to the tool’s input parameters (e.g. to share info between tools)
- `outputs_to_state` (optional) specifies how tool outputs are written back into the agent state, with optional handlers.

Keep in mind that the accurate definitions of `name` and `description` are important for the Language Model to prepare the call correctly.

`Tool` exposes a `tool_spec` property, returning the tool specification to be used by Language Models.

It also has an `invoke` method that executes the underlying function with the provided parameters.

## Tool Initialization

Here is how to initialize a Tool to work with a specific function:

```python
from haystack.tools import Tool

def add(a: int, b: int) -> int:
    return a + b

parameters = {
    "type": "object",
    "properties": {
        "a": {"type": "integer"},
        "b": {"type": "integer"}
    },
    "required": ["a", "b"]
}

add_tool = Tool(name="addition_tool",
            description="This tool adds two numbers",
            parameters=parameters,
            function=add)

print(add_tool.tool_spec)

print(add_tool.invoke(a=15, b=10))
```

```
{'name': 'addition_tool',
    'description': 'This tool adds two numbers',
    'parameters':{'type': 'object',
        'properties':{'a':{'type': 'integer'}, 'b':{'type': 'integer'}},
        'required':['a', 'b']}}

25
```

### Advanced Tool Configuration

`outputs_to_string` and `outputs_to_state` let you control how a tool’s outputs are surfaced to the LLM and stored in the agent state.

Use them to format structured outputs for the LLM while keeping raw data available for later steps.

```python
from haystack.tools import Tool

def format_documents(documents):
    return "\n".join(f"{i+1}. Document: {doc.content}" for i, doc in enumerate(documents))

def format_summary(metadata):
    return f"Found {metadata['count']} results"

tool = Tool(
    name="search",
    description="Search for documents",
    parameters={...},
    function=search_func,  # Returns {"documents": [Document(...)], "metadata": {"count": 5}, "debug_info": {...}}
    outputs_to_string={
        "formatted_docs": {"source": "documents", "handler": format_documents},
        "summary": {"source": "metadata", "handler": format_summary}
    }
    outputs_to_state={"documents": {"source": "documents"}}, # Save Documents into Agent's state
)

# After the tool invocation, the tool result includes:
# {
#     "formatted_docs": "1. Document Title\n   Content...\n2. ...",
#     "summary": "Found 5 results"
# }
```
After invocation, only the configured string outputs are returned to the LLM, while selected fields through `outputs_to_state` (like documents) are saved in the agent state.

### @tool decorator

The `@tool` decorator simplifies converting a function into a Tool. It infers Tool name, description, and parameters from the function and automatically generates a JSON schema. It uses Python's `typing.Annotated` for the description of parameters. If you need to customize Tool name and description, use `create_tool_from_function` instead.

```python
from typing import Annotated, Literal
from haystack.tools import tool

@tool
def get_weather(
  city: Annotated[str, "the city for which to get the weather"] = "Munich",
  unit: Annotated[Literal["Celsius", "Fahrenheit"], "the unit for the temperature"] = "Celsius"):
  '''A simple function to get the current weather for a location.'''
  return f"Weather report for {city}: 20 {unit}, sunny"

print(get_weather)
```

```
Tool(name='get_weather', description='A simple function to get the current weather for a location.',
parameters={
'type': 'object',
'properties': {
    'city': {'type': 'string', 'description': 'the city for which to get the weather', 'default': 'Munich'},
    'unit': {
        'type': 'string',
        'enum': ['Celsius', 'Fahrenheit'],
        'description': 'the unit for the temperature',
        'default': 'Celsius',
    },
    }
},
function=<function get_weather at 0x7f7b3a8a9b80>)
```

### create_tool_from_function

The `create_tool_from_function` method provides more flexibility than the`@tool` decorator and allows setting Tool name and description. It infers the Tool parameters automatically and generates a JSON schema automatically in the same way as the `@tool` decorator.

```python
from typing import Annotated, Literal
from haystack.tools import create_tool_from_function

def get_weather(
  city: Annotated[str, "the city for which to get the weather"] = "Munich",
  unit: Annotated[Literal["Celsius", "Fahrenheit"], "the unit for the temperature"] = "Celsius"):
  '''A simple function to get the current weather for a location.'''
  return f"Weather report for {city}: 20 {unit}, sunny"

tool = create_tool_from_function(get_weather)

print(tool)
```

```
Tool(name='get_weather', description='A simple function to get the current weather for a location.',
parameters={
'type': 'object',
'properties': {
    'city': {'type': 'string', 'description': 'the city for which to get the weather', 'default': 'Munich'},
    'unit': {
        'type': 'string',
        'enum': ['Celsius', 'Fahrenheit'],
        'description': 'the unit for the temperature',
        'default': 'Celsius',
    },
    }
},
function=<function get_weather at 0x7f7b3a8a9b80>)
```

## Toolset

A Toolset groups multiple Tool instances into a single manageable unit. It simplifies the passing of tools to components like Chat Generators or `ToolInvoker`, and supports filtering, serialization, and reuse.

```python
from haystack.tools import Toolset

math_toolset = Toolset([add_tool, subtract_tool])
```

See more details and examples on the [Toolset documentation page](toolset.mdx).

## Usage

To better understand this section, make sure you are also familiar with Haystack's [`ChatMessage`](../concepts/data-classes/chatmessage.mdx) data class.

### Passing Tools to a Chat Generator

Using the `tools` parameter, you can pass tools as a list of Tool instances or a single Toolset during initialization or in the `run` method. Tools passed at runtime override those set at initialization.

:::info Chat Generators support

Not all Chat Generators currently support tools, but we are actively expanding tool support across more models.

Look out for the `tools` parameter in a specific Chat Generator's `__init__` and `run` methods.
:::

```python
from haystack.dataclasses import ChatMessage
from haystack.components.generators.chat import OpenAIChatGenerator

## Initialize the Chat Generator with the addition tool
chat_generator = OpenAIChatGenerator(model="gpt-4o-mini", tools=[add_tool])

## here we expect the Tool to be invoked
res=chat_generator.run([ChatMessage.from_user("10 + 238")])
print(res)

## here the model can respond without using the Tool
res=chat_generator.run([ChatMessage.from_user("What is the habitat of a lion?")])
print(res)
```

```
{'replies':[ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>,
            _content=[ToolCall(tool_name='addition_tool',
                    arguments={'a':10, 'b':238},
                    id='call_rbYtbCdW0UbWMfy2x0sgF1Ap'
)],
            _meta={...})]}


{'replies':[ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>,
            _content=[TextContent(text='Lions primarily inhabit grasslands, savannas, and open woodlands. ...'
)],
            _meta={...})]}
```

The same result of the previous run can be achieved by passing tools at runtime:

```python
## Initialize the Chat Generator without tools
chat_generator = OpenAIChatGenerator(model="gpt-4o-mini")

## pass tools in the run method
res_w_tool_call=chat_generator.run([ChatMessage.from_user("10 + 238")], tools=math_toolset)
print(res_w_tool_call)
```

### Executing Tool Calls

To execute prepared tool calls, you can use the [`ToolInvoker`](../pipeline-components/tools/toolinvoker.mdx) component. This component acts as the execution engine for tools, processing the calls prepared by the Language Model.

Here's an example:

```python
import random
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.tools import ToolInvoker
from haystack.tools import Tool

## Define a dummy weather toolimport random

def dummy_weather(location: str):
    return {"temp": f"{random.randint(-10,40)} °C",
            "humidity": f"{random.randint(0,100)}%"}

weather_tool = Tool(
    name="weather",
    description="A tool to get the weather",
    function=dummy_weather,
    parameters={
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"],
    },
)

## Initialize the Chat Generator with the weather tool
chat_generator = OpenAIChatGenerator(model="gpt-4o-mini", tools=[weather_tool])

## Initialize the Tool Invoker with the weather tool
tool_invoker = ToolInvoker(tools=[weather_tool])

user_message = ChatMessage.from_user("What is the weather in Berlin?")

replies = chat_generator.run(messages=[user_message])["replies"]
print(f"assistant messages: {replies}")

## If the assistant message contains a tool call, run the tool invoker
if replies[0].tool_calls:
    tool_messages = tool_invoker.run(messages=replies)["tool_messages"]
    print(f"tool messages: {tool_messages}")
```

```
assistant messages:[ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>, _content=[ToolCall(tool_name='weather',
arguments={'location': 'Berlin'}, id='call_YEvCEAmlvc42JGXV84NU8wtV')], _meta={'model': 'gpt-4o-mini-2024-07-18',
'index':0, 'finish_reason': 'tool_calls', 'usage':{'completion_tokens':13, 'prompt_tokens':50, 'total_tokens':
63}})]

tool messages: [ChatMessage(_role=<ChatRole.TOOL: 'tool'>, _content=[ToolCallResult(result="{'temp': '22 °C',
'humidity': '35%'}", origin=ToolCall(tool_name='weather', arguments={'location': 'Berlin'},
id='call_YEvCEAmlvc42JGXV84NU8wtV'), error=False)], _meta={})]
```

### Processing Tool Results with the Chat Generator

In some cases, the raw output from a tool may not be immediately suitable for the end user.

You can refine the tool’s response by passing it back to the Chat Generator. This generates a user-friendly and conversational message.

In this example, we’ll pass the tool’s response back to the Chat Generator for final processing:

```python
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.tools import ToolInvoker
from haystack.tools import Tool

## Define a dummy weather toolimport random

def dummy_weather(location: str):
    return {"temp": f"{random.randint(-10,40)} °C",
            "humidity": f"{random.randint(0,100)}%"}

weather_tool = Tool(
    name="weather",
    description="A tool to get the weather",
    function=dummy_weather,
    parameters={
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"],
    },
)

chat_generator = OpenAIChatGenerator(model="gpt-4o-mini", tools=[weather_tool])
tool_invoker = ToolInvoker(tools=[weather_tool])

user_message = ChatMessage.from_user("What is the weather in Berlin?")

replies = chat_generator.run(messages=[user_message])["replies"]
print(f"assistant messages: {replies}")

if replies[0].tool_calls:
    tool_messages = tool_invoker.run(messages=replies)["tool_messages"]
    print(f"tool messages: {tool_messages}")
    # we pass all the messages to the Chat Generator
    messages = [user_message] + replies + tool_messages
    final_replies = chat_generator.run(messages=messages)["replies"]
    print(f"final assistant messages: {final_replies}")
```

```
assistant messages:[ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>, _content=[ToolCall(tool_name='weather',
arguments={'location': 'Berlin'}, id='call_jHX0RCDHRKX7h8V9RrNs6apy')], _meta={'model': 'gpt-4o-mini-2024-07-18',
'index':0, 'finish_reason': 'tool_calls', 'usage':{'completion_tokens':13, 'prompt_tokens':50, 'total_tokens':
63}})]

tool messages: [ChatMessage(_role=<ChatRole.TOOL: 'tool'>, _content=[ToolCallResult(result="{'temp': '2 °C',
'humidity': '15%'}", origin=ToolCall(tool_name='weather', arguments={'location': 'Berlin'},
id='call_jHX0RCDHRKX7h8V9RrNs6apy'), error=False)], _meta={})]

final assistant messages: [ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>, _content=[TextContent(text='The
current weather in Berlin is 2 °C with a humidity level of 15%.')], _meta={'model': 'gpt-4o-mini-2024-07-18',
'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 19, 'prompt_tokens': 85, 'total_tokens':
104}})]
```

### Passing Tools to Agent

You can also use `Tool` with the [Agent](../pipeline-components/agents-1/agent.mdx) component. Internally, the `Agent` component includes a `ToolInvoker` and the ChatGenerator of your choice to execute tool calls and process tool results.

```python
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools.tool import Tool
from haystack.components.agents import Agent
from typing import List

## Tool Function
def calculate(expression: str) -> dict:
    try:
        result = eval(expression, {"__builtins__": {}})
        return {"result": result}
    except Exception as e:
        return {"error": str(e)}

## Tool Definition
calculator_tool = Tool(
    name="calculator",
    description="Evaluate basic math expressions.",
    parameters={
        "type": "object",
        "properties": {
            "expression": {"type": "string", "description": "Math expression to evaluate"}
        },
        "required": ["expression"]
    },
    function=calculate,
    outputs_to_state={"calc_result": {"source": "result"}}
)

## Agent Setup
agent = Agent(
    chat_generator=OpenAIChatGenerator(),
    tools=[calculator_tool],
    exit_conditions=["calculator"],
    state_schema={
        "calc_result": {"type": int},
    }
)

## Run the Agent
response = agent.run(messages=[ChatMessage.from_user("What is 7 * (4 + 2)?")])

## Output
print(response["messages"])
print("Calc Result:", response.get("calc_result"))
```

## Additional References

🧑‍🍳 Cookbooks:

- [Build a GitHub Issue Resolver Agent](https://haystack.deepset.ai/cookbook/github_issue_resolver_agent)
- [Newsletter Sending Agent with Haystack Tools](https://haystack.deepset.ai/cookbook/newsletter-agent)
- [Create a Swarm of Agents](https://haystack.deepset.ai/cookbook/swarm)
