Tools

component_tool

ComponentTool

Bases: Tool

A Tool that wraps Haystack components, allowing them to be used as tools by LLMs.

ComponentTool automatically generates LLM-compatible tool schemas from component input sockets, which are derived from the component's run method signature and type hints.

Key features:

• Automatic LLM tool calling schema generation from component input sockets
• Type conversion and validation for component inputs
• Support for types:
  • Dataclasses
  • Lists of dataclasses
  • Basic types (str, int, float, bool, dict)
  • Lists of basic types
• Automatic name generation from component class name
• Description extraction from component docstrings

To use ComponentTool, you first need a Haystack component - either an existing one or a new one you create. You can create a ComponentTool from the component by passing the component to the ComponentTool constructor. Below is an example of creating a ComponentTool from an existing SerperDevWebSearch component from the serperdev-haystack integration package (pip install serperdev-haystack).

Usage Example:

python

from haystack import component
from haystack.tools import ComponentTool
from haystack.utils import Secret
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.websearch.serperdev import SerperDevWebSearch

# Create a SerperDev search component
search = SerperDevWebSearch(api_key=Secret.from_env_var("SERPERDEV_API_KEY"), top_k=3)

# Create a tool from the component
tool = ComponentTool(
    component=search,
    name="web_search",  # Optional: defaults to "serper_dev_web_search"
    description="Search the web for current information on any topic"  # Optional: defaults to component docstring
)

# Create an Agent with an OpenAIChatGenerator and the tool
agent = Agent(chat_generator=OpenAIChatGenerator(), tools=[tool])

message = ChatMessage.from_user("Use the web search tool to find information about Nikola Tesla")

# Run the Agent
result = agent.run(messages=[message])

print(result)

init

python

__init__(
    component: Component,
    name: str | None = None,
    description: str | None = None,
    parameters: dict[str, Any] | None = None,
    *,
    outputs_to_string: dict[str, str | Callable[[Any], str]] | None = None,
    inputs_from_state: dict[str, str] | None = None,
    outputs_to_state: dict[str, dict[str, str | Callable]] | None = None
) -> None

Create a Tool instance from a Haystack component.

Parameters:

• component (Component) – The Haystack component to wrap as a tool.
• name (str | None) – Optional name for the tool (defaults to snake_case of component class name).
• description (str | None) – Optional description (defaults to component's docstring).
• parameters (dict[str, Any] | None) – A JSON schema defining the parameters expected by the Tool. Will fall back to the parameters defined in the component's run method signature if not provided.
• outputs_to_string (dict[str, str | Callable\[[Any], str]] | None) – Optional dictionary defining how tool outputs should be converted into string(s) or results. If not provided, the tool result is converted to a string using a default handler.

outputs_to_string supports two formats:

1. Single output format - use "source", "handler", and/or "raw_result" at the root level:

   python

   {
       "source": "docs", "handler": format_documents, "raw_result": False
   }

   • source: If provided, only the specified output key is sent to the handler.
   • handler: A function that takes the tool output (or the extracted source value) and returns the final result.
   • raw_result: If True, the result is returned raw without string conversion, but applying the handler if provided. This is intended for tools that return images. In this mode, the Tool function or the handler function must return a list of TextContent/ImageContent objects to ensure compatibility with Chat Generators.

2. Multiple output format - map keys to individual configurations:

   python

   {
       "formatted_docs": {"source": "docs", "handler": format_documents},
       "summary": {"source": "summary_text", "handler": str.upper}
   }

   Each key maps to a dictionary that can contain "source" and/or "handler". Note that raw_result is not supported in the multiple output format.

• inputs_from_state (dict[str, str] | None) – Optional dictionary mapping state keys to tool parameter names. Example: {"repository": "repo"} maps state's "repository" to tool's "repo" parameter.
• outputs_to_state (dict[str, dict[str, str | Callable]] | None) – Optional dictionary defining how tool outputs map to keys within state as well as optional handlers. If the source is provided only the specified output key is sent to the handler. Example:

python

{
    "documents": {"source": "docs", "handler": custom_handler}
}

If the source is omitted the whole tool result is sent to the handler. Example:

python

{
    "documents": {"handler": custom_handler}
}

Raises:

• TypeError – If the object passed is not a Haystack Component instance.
• ValueError – If the component has already been added to a pipeline, or if schema generation fails.

warm_up

python

warm_up() -> None

Prepare the ComponentTool for use.

to_dict

python

to_dict() -> dict[str, Any]

Serializes the ComponentTool to a dictionary.

from_dict

python

from_dict(data: dict[str, Any]) -> ComponentTool

Deserializes the ComponentTool from a dictionary.

from_function

create_tool_from_function

python

create_tool_from_function(
    function: Callable,
    name: str | None = None,
    description: str | None = None,
    inputs_from_state: dict[str, str] | None = None,
    outputs_to_state: dict[str, dict[str, Any]] | None = None,
    outputs_to_string: dict[str, Any] | None = None,
) -> Tool

Create a Tool instance from a function.

Allows customizing the Tool name and description. For simpler use cases, consider using the @tool decorator.

Usage example

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>)

Parameters:

• function (Callable) – The function to be converted into a Tool. May be either a regular function (assigned to the resulting Tool's function field) or a coroutine function defined with async def (assigned to async_function). The function must include type hints for all parameters. The function is expected to have basic python input types (str, int, float, bool, list, dict, tuple). Other input types may work but are not guaranteed. If a parameter is annotated using typing.Annotated, its metadata will be used as parameter description.
• name (str | None) – The name of the Tool. If not provided, the name of the function will be used.
• description (str | None) – The description of the Tool. If not provided, the docstring of the function will be used. To intentionally leave the description empty, pass an empty string.
• inputs_from_state (dict[str, str] | None) – Optional dictionary mapping state keys to tool parameter names. Example: {"repository": "repo"} maps state's "repository" to tool's "repo" parameter.
• outputs_to_state (dict[str, dict[str, Any]] | None) – Optional dictionary defining how tool outputs map to keys within state as well as optional handlers. If the source is provided only the specified output key is sent to the handler. Example:

python

{
    "documents": {"source": "docs", "handler": custom_handler}
}

If the source is omitted the whole tool result is sent to the handler. Example:

python

{
    "documents": {"handler": custom_handler}
}

• outputs_to_string (dict[str, Any] | None) – Optional dictionary defining how tool outputs should be converted into string(s) or results. If not provided, the tool result is converted to a string using a default handler.

outputs_to_string supports two formats:

1. Single output format - use "source", "handler", and/or "raw_result" at the root level:

   python

   {
       "source": "docs", "handler": format_documents, "raw_result": False
   }

   • source: If provided, only the specified output key is sent to the handler. If not provided, the whole tool result is sent to the handler.
   • handler: A function that takes the tool output (or the extracted source value) and returns the final result.
   • raw_result: If True, the result is returned raw without string conversion, but applying the handler if provided. This is intended for tools that return images. In this mode, the Tool function or the handler must return a list of TextContent/ImageContent objects to ensure compatibility with Chat Generators.

2. Multiple output format - map keys to individual configurations:

   python

   {
       "formatted_docs": {"source": "docs", "handler": format_documents},
       "summary": {"source": "summary_text", "handler": str.upper}
   }

   Each key maps to a dictionary that can contain "source" and/or "handler". Note that raw_result is not supported in the multiple output format.

Returns:

• Tool – The Tool created from the function.

Raises:

• ValueError – If any parameter of the function lacks a type hint.
• SchemaGenerationError – If there is an error generating the JSON schema for the Tool.

tool

python

tool(
    function: Callable | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    inputs_from_state: dict[str, str] | None = None,
    outputs_to_state: dict[str, dict[str, Any]] | None = None,
    outputs_to_string: dict[str, Any] | None = None
) -> Tool | Callable[[Callable], Tool]

Decorator to convert a function into a Tool.

Can be used with or without parameters: @tool # without parameters def my_function(): ...

@tool(name="custom_name") # with parameters def my_function(): ...

Usage example

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>)

Parameters:

• function (Callable | None) – The function to decorate (when used without parameters)
• name (str | None) – Optional custom name for the tool
• description (str | None) – Optional custom description
• inputs_from_state (dict[str, str] | None) – Optional dictionary mapping state keys to tool parameter names. Example: {"repository": "repo"} maps state's "repository" to tool's "repo" parameter.
• outputs_to_state (dict[str, dict[str, Any]] | None) – Optional dictionary defining how tool outputs map to keys within state as well as optional handlers. If the source is provided only the specified output key is sent to the handler. Example:

python

{
    "documents": {"source": "docs", "handler": custom_handler}
}

If the source is omitted the whole tool result is sent to the handler. Example:

python

{
    "documents": {"handler": custom_handler}
}

• outputs_to_string (dict[str, Any] | None) – Optional dictionary defining how tool outputs should be converted into string(s) or results. If not provided, the tool result is converted to a string using a default handler.

outputs_to_string supports two formats:

1. Single output format - use "source", "handler", and/or "raw_result" at the root level:

   python

   {
       "source": "docs", "handler": format_documents, "raw_result": False
   }

   • source: If provided, only the specified output key is sent to the handler. If not provided, the whole tool result is sent to the handler.
   • handler: A function that takes the tool output (or the extracted source value) and returns the final result.
   • raw_result: If True, the result is returned raw without string conversion, but applying the handler if provided. This is intended for tools that return images. In this mode, the Tool function or the handler must return a list of TextContent/ImageContent objects to ensure compatibility with Chat Generators.

2. Multiple output format - map keys to individual configurations:

   python

   {
       "formatted_docs": {"source": "docs", "handler": format_documents},
       "summary": {"source": "summary_text", "handler": str.upper}
   }

   Each key maps to a dictionary that can contain "source" and/or "handler". Note that raw_result is not supported in the multiple output format.

Returns:

• Tool | Callable\[[Callable], Tool] – Either a Tool instance or a decorator function that will create one

pipeline_tool

PipelineTool

Bases: ComponentTool

A Tool that wraps Haystack Pipelines, allowing them to be used as tools by LLMs.

PipelineTool automatically generates LLM-compatible tool schemas from pipeline input sockets, which are derived from the underlying components in the pipeline.

Key features:

• Automatic LLM tool calling schema generation from pipeline inputs
• Description extraction of pipeline inputs based on the underlying component docstrings

To use PipelineTool, you first need a Haystack pipeline. Below is an example of creating a PipelineTool

Usage Example:

python

from haystack import Document, Pipeline
from haystack.dataclasses import ChatMessage
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import OpenAITextEmbedder, OpenAIDocumentEmbedder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.retrievers import InMemoryEmbeddingRetriever
from haystack.components.agents import Agent
from haystack.tools import PipelineTool

# Initialize a document store and add some documents
document_store = InMemoryDocumentStore()
document_embedder = OpenAIDocumentEmbedder()
documents = [
    Document(content="Nikola Tesla was a Serbian-American inventor and electrical engineer."),
    Document(
        content="He is best known for his contributions to the design of the modern alternating current (AC) "
                "electricity supply system."
    ),
]
docs_with_embeddings = document_embedder.run(documents=documents)["documents"]
document_store.write_documents(docs_with_embeddings)

# Build a simple retrieval pipeline
retrieval_pipeline = Pipeline()
retrieval_pipeline.add_component("embedder", OpenAITextEmbedder())
retrieval_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))

retrieval_pipeline.connect("embedder.embedding", "retriever.query_embedding")

# Wrap the pipeline as a tool
retriever_tool = PipelineTool(
    pipeline=retrieval_pipeline,
    input_mapping={"query": ["embedder.text"]},
    output_mapping={"retriever.documents": "documents"},
    name="document_retriever",
    description="For any questions about Nikola Tesla, always use this tool",
)

# Create an Agent with the tool
agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4.1-mini"),
    tools=[retriever_tool]
)

# Let the Agent handle a query
result = agent.run([ChatMessage.from_user("Who was Nikola Tesla?")])

# Print result of the tool call
print("Tool Call Result:")
print(result["messages"][2].tool_call_result.result)
print("")

# Print answer
print("Answer:")
print(result["messages"][-1].text)

init

python

__init__(
    pipeline: Pipeline,
    *,
    name: str,
    description: str,
    input_mapping: dict[str, list[str]] | None = None,
    output_mapping: dict[str, str] | None = None,
    parameters: dict[str, Any] | None = None,
    outputs_to_string: dict[str, str | Callable[[Any], str]] | None = None,
    inputs_from_state: dict[str, str] | None = None,
    outputs_to_state: dict[str, dict[str, str | Callable]] | None = None
) -> None

Create a Tool instance from a Haystack pipeline.

Parameters:

• pipeline (Pipeline) – The Haystack pipeline to wrap as a tool.
• name (str) – Name of the tool.
• description (str) – Description of the tool.
• input_mapping (dict[str, list[str]] | None) – A dictionary mapping component input names to pipeline input socket paths. If not provided, a default input mapping will be created based on all pipeline inputs. Example:

python

input_mapping={
    "query": ["retriever.query", "prompt_builder.query"],
}

• output_mapping (dict[str, str] | None) – A dictionary mapping pipeline output socket paths to component output names. If not provided, a default output mapping will be created based on all pipeline outputs. Example:

python

output_mapping={
    "retriever.documents": "documents",
    "generator.replies": "replies",
}

• parameters (dict[str, Any] | None) – A JSON schema defining the parameters expected by the Tool. Will fall back to the parameters defined in the component's run method signature if not provided.
• outputs_to_string (dict[str, str | Callable\[[Any], str]] | None) – Optional dictionary defining how tool outputs should be converted into string(s) or results. If not provided, the tool result is converted to a string using a default handler.

outputs_to_string supports two formats:

1. Single output format - use "source", "handler", and/or "raw_result" at the root level:

   python

   {
       "source": "docs", "handler": format_documents, "raw_result": False
   }

   • source: If provided, only the specified output key is sent to the handler.
   • handler: A function that takes the tool output (or the extracted source value) and returns the final result.
   • raw_result: If True, the result is returned raw without string conversion, but applying the handler if provided. This is intended for tools that return images. In this mode, the Tool function or the handler function must return a list of TextContent/ImageContent objects to ensure compatibility with Chat Generators.

2. Multiple output format - map keys to individual configurations:

   python

   {
       "formatted_docs": {"source": "docs", "handler": format_documents},
       "summary": {"source": "summary_text", "handler": str.upper}
   }

   Each key maps to a dictionary that can contain "source" and/or "handler". Note that raw_result is not supported in the multiple output format.

• inputs_from_state (dict[str, str] | None) – Optional dictionary mapping state keys to tool parameter names. Example: {"repository": "repo"} maps state's "repository" to tool's "repo" parameter.
• outputs_to_state (dict[str, dict[str, str | Callable]] | None) – Optional dictionary defining how tool outputs map to keys within state as well as optional handlers. If the source is provided only the specified output key is sent to the handler. Example:

python

{
    "documents": {"source": "docs", "handler": custom_handler}
}

If the source is omitted the whole tool result is sent to the handler. Example:

python

{
    "documents": {"handler": custom_handler}
}

Raises:

• ValueError – If the provided pipeline is not a valid Haystack Pipeline instance.

to_dict

python

to_dict() -> dict[str, Any]

Serializes the PipelineTool to a dictionary.

Returns:

• dict[str, Any] – The serialized dictionary representation of PipelineTool.

from_dict

python

from_dict(data: dict[str, Any]) -> PipelineTool

Deserializes the PipelineTool from a dictionary.

Parameters:

• data (dict[str, Any]) – The dictionary representation of PipelineTool.

Returns:

• PipelineTool – The deserialized PipelineTool instance.

searchable_toolset

SearchableToolset

Bases: Toolset

Dynamic tool discovery from large catalogs using BM25 search.

This Toolset enables LLMs to discover and use tools from large catalogs through BM25-based search. Instead of exposing all tools at once (which can overwhelm the LLM context), it provides a search_tools bootstrap tool that allows the LLM to find and load specific tools as needed.

For very small catalogs (below search_threshold), acts as a simple passthrough exposing all tools directly without any discovery mechanism.

Usage Example

python

from typing import Annotated

from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools import SearchableToolset, tool

@tool
def get_weather(city: Annotated[str, "The city to get the weather for"]) -> str:
    '''Get the current weather for a city.'''
    return f"The weather in {city} is 22°C and sunny."

@tool
def search_web(query: Annotated[str, "The query to search the web for"]) -> str:
    '''Search the web for a query.'''
    return f"Top result for '{query}': ..."

@tool
def convert_currency(
    amount: Annotated[float, "The amount to convert"],
    to_currency: Annotated[str, "The currency to convert to, e.g. 'EUR'"],
) -> str:
    '''Convert an amount in USD to another currency.'''
    return f"{amount} USD is {amount * 0.9} {to_currency}"

# search_threshold=2 means a catalog of 2+ tools activates discovery: the agent only sees the
# `search_tools` tool and must search to load the others (set it higher for larger catalogs).
toolset = SearchableToolset(catalog=[get_weather, search_web, convert_currency], search_threshold=2)

agent = Agent(chat_generator=OpenAIChatGenerator(), tools=toolset)

# The agent is initially provided only with the search_tools tool and will use it to find relevant tools.
result = agent.run(messages=[ChatMessage.from_user("What's the weather in Milan?")])
print(result["last_message"].text)

init

python

__init__(
    catalog: ToolsType,
    *,
    top_k: int = 3,
    search_threshold: int = 8,
    search_tool_name: str = "search_tools",
    search_tool_description: str | None = None,
    search_tool_parameters_description: dict[str, str] | None = None
) -> None

Initialize the SearchableToolset.

Parameters:

• catalog (ToolsType) – Source of tools - a list of Tools, list of Toolsets, or a single Toolset.
• top_k (int) – Default number of results for search_tools.
• search_threshold (int) – Minimum catalog size to activate search. If catalog has fewer tools, acts as passthrough (all tools visible). Default is 8.
• search_tool_name (str) – Custom name for the bootstrap search tool. Default is "search_tools".
• search_tool_description (str | None) – Custom description for the bootstrap search tool. If not provided, uses a default description.
• search_tool_parameters_description (dict[str, str] | None) – Custom descriptions for the bootstrap search tool's parameters. Keys must be a subset of {"tool_keywords", "k"}. Example: {"tool_keywords": "Keywords to find tools, e.g. 'email send'"}

add

python

add(tool: Tool | Toolset) -> None

Adding new tools after initialization is not supported for SearchableToolset.

warm_up

python

warm_up() -> None

Prepare the toolset for use.

Warms up the catalog (so lazy toolsets like MCPToolset can connect) and flattens it. Above the passthrough threshold, it also indexes the catalog and creates the search_tools bootstrap tool.

This method is idempotent: it only warms up the toolset the first time it is called.

Raises:

• ValueError – If the flattened catalog contains tools with duplicate names.

get_selectable_tools

python

get_selectable_tools() -> list[Tool]

Return the full catalog of tools that can be selected by name.

Iteration only exposes the search tool plus already-discovered tools, but name-based selection can target any tool in the catalog, so this returns the entire flattened catalog (warming up first if needed).

Returns:

• list[Tool] – The flattened catalog of tools.

clear

python

clear() -> None

Clear all discovered tools.

This method allows resetting the toolset's discovered tools between agent runs when the same toolset instance is reused. This can be useful for long-running applications to control memory usage or to start fresh searches.

spawn

python

spawn() -> SearchableToolset

Return an isolated copy for a single run.

The copy shares the read-only catalog and BM25 index but gets fresh discovered tools and name selection, plus a bootstrap search tool bound to the copy. This way concurrent runs sharing the same configured SearchableToolset don't share discovered tools or collide on the active selection.

Returns:

• SearchableToolset – A run-scoped copy of this SearchableToolset.

to_dict

python

to_dict() -> dict[str, Any]

Serialize the toolset to a dictionary.

Returns:

• dict[str, Any] – Dictionary representation of the toolset.

from_dict

python

from_dict(data: dict[str, Any]) -> SearchableToolset

Deserialize a toolset from a dictionary.

Parameters:

• data (dict[str, Any]) – Dictionary representation of the toolset.

Returns:

• SearchableToolset – New SearchableToolset instance.

Raises:

• TypeError – If a serialized catalog entry is not a subclass of Tool or Toolset.

skills/skill_toolset

SkillToolset

Bases: Toolset

A Toolset that lets an Agent discover and read skills via progressive disclosure.

A skill is a directory (or equivalent storage unit) containing a SKILL.md file with YAML frontmatter (name and description) and a markdown body of instructions. Skills may bundle additional files (reference docs, examples, templates).

• On warm_up, the name and description of every discovered skill are baked into the load_skill tool description so the model knows which skills exist without any system prompt injection.
• load_skill returns a skill's full instructions on demand, plus a manifest of its bundled files.
• read_skill_file reads a bundled file on demand.

Usage example

python

from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools import SkillToolset
from haystack.skill_stores.file_system import FileSystemSkillStore

store = FileSystemSkillStore("skills/")
skills_toolset = SkillToolset(store)
agent = Agent(chat_generator=OpenAIChatGenerator(), tools=skills_toolset)
result = agent.run(messages=[ChatMessage.from_user("Fill in this PDF form for me.")])

Expected filesystem layout:

skills/
  pdf-forms/
    SKILL.md            # frontmatter (name, description) + markdown instructions
    reference/forms.md

The tool names load_skill and read_skill_file are fixed, so an Agent can use at most one SkillToolset. To serve skills from multiple sources, back a single toolset with a custom store that merges them.

init

python

__init__(store: SkillStore) -> None

Initialize the SkillToolset.

Constructing the toolset does not read any skills. The store is queried for the available skills on warm_up(), so stores that do I/O (reading a directory, connecting to a database) stay cheap to construct.

The load_skill and read_skill_file tools are created right away, so the toolset can be used as a collection (length, membership checks, iteration) immediately.

Parameters:

• store (SkillStore) – A haystack.skill_stores.types.SkillStore instance to back this toolset.

skills

python

skills: dict[str, SkillInfo]

Mapping of skill name to its metadata. Triggers warm_up() on first access if not already warmed up.

warm_up

python

warm_up() -> None

Discover the available skills from the store and bake the catalog into the load_skill description.

Only the description content is dynamic, so the (static) tools created in __init__ are reused; this refreshes load_skill's description once the catalog is known. Idempotent: repeated calls after the first are no-ops.

add

python

add(tool: Tool | Toolset) -> None

Adding tools is not supported: a SkillToolset's tools are fixed and defined by its store.

to_dict

python

to_dict() -> dict[str, Any]

Serialize the toolset to a dictionary.

Returns:

• dict[str, Any] – Dictionary representation of the toolset.

from_dict

python

from_dict(data: dict[str, Any]) -> SkillToolset

Deserialize a toolset from a dictionary.

Parameters:

• data (dict[str, Any]) – Dictionary representation of the toolset, as produced by to_dict.

Returns:

• SkillToolset – A new SkillToolset instance.

tool

Tool

Data class representing a Tool that Language Models can prepare a call for.

Accurate definitions of the textual attributes such as name and description are important for the Language Model to correctly prepare the call.

For resource-intensive operations like establishing connections to remote services or loading models, override the warm_up() method. This method is called before the Tool is used and should be idempotent, as it may be called multiple times during pipeline/agent setup.

Parameters:

• name (str) – Name of the Tool.
• description (str) – Description of the Tool.
• parameters (dict[str, Any]) – A JSON schema defining the parameters expected by the Tool.
• function (Callable | None) – The synchronous function invoked by Tool.invoke. Must be a regular function — coroutine functions should be passed to async_function instead. Either function or async_function (or both) must be set.
• async_function (Callable | None) – Optional coroutine function awaited by Tool.invoke_async. When only async_function is set, invoke raises a ToolInvocationError. When only function is set, invoke_async falls back to running function in a worker thread via asyncio.to_thread.
• outputs_to_string (dict[str, Any] | None) – Optional dictionary defining how tool outputs should be converted into string(s) or results. If not provided, the tool result is converted to a string using a default handler.

outputs_to_string supports two formats:

1. Single output format - use "source", "handler", and/or "raw_result" at the root level:

   python

   {
       "source": "docs", "handler": format_documents, "raw_result": False
   }

   • source: If provided, only the specified output key is sent to the handler. If not provided, the whole tool result is sent to the handler.
   • handler: A function that takes the tool output (or the extracted source value) and returns the final result.
   • raw_result: If True, the result is returned raw without string conversion, but applying the handler if provided. This is intended for tools that return images. In this mode, the Tool function or the handler must return a list of TextContent/ImageContent objects to ensure compatibility with Chat Generators.

2. Multiple output format - map keys to individual configurations:

   python

   {
       "formatted_docs": {"source": "docs", "handler": format_documents},
       "summary": {"source": "summary_text", "handler": str.upper}
   }

   Each key maps to a dictionary that can contain "source" and/or "handler". Note that raw_result is not supported in the multiple output format.

• inputs_from_state (dict[str, str] | None) – Optional dictionary mapping state keys to tool parameter names. Example: {"repository": "repo"} maps state's "repository" to tool's "repo" parameter.
• outputs_to_state (dict[str, dict[str, Any]] | None) – Optional dictionary defining how tool outputs map to keys within state as well as optional handlers. If the source is provided only the specified output key is sent to the handler. Example:

python

{
    "documents": {"source": "docs", "handler": custom_handler}
}

If the source is omitted the whole tool result is sent to the handler. Example:

python

{
    "documents": {"handler": custom_handler}
}

Raises:

• ValueError – If neither function nor async_function is provided, if function is a coroutine function, if async_function is not a coroutine function, if parameters is not a valid JSON schema, or if the outputs_to_state, outputs_to_string, or inputs_from_state configurations are invalid.
• TypeError – If any configuration value in outputs_to_state, outputs_to_string, or inputs_from_state has the wrong type.

tool_spec

python

tool_spec: dict[str, Any]

Return the Tool specification to be used by the Language Model.

warm_up

python

warm_up() -> None

Prepare the Tool for use.

Override this method to establish connections to remote services, load models, or perform other resource-intensive initialization. This method should be idempotent, as it may be called multiple times.

invoke

python

invoke(**kwargs: Any) -> Any

Invoke the Tool synchronously with the provided keyword arguments.

Raises:

• ToolInvocationError – If the Tool has no sync function, or if the underlying call raises an exception.

invoke_async

python

invoke_async(**kwargs: Any) -> Any

Invoke the Tool asynchronously with the provided keyword arguments.

If async_function is set, it is awaited directly. Otherwise the sync function is dispatched to a worker thread via asyncio.to_thread, which propagates the current context to the worker.

Raises:

• ToolInvocationError – If the underlying call raises an exception.

to_dict

python

to_dict() -> dict[str, Any]

Serializes the Tool to a dictionary.

Returns:

• dict[str, Any] – Dictionary with serialized data.

from_dict

python

from_dict(data: dict[str, Any]) -> Tool

Deserializes the Tool from a dictionary.

Parameters:

• data (dict[str, Any]) – Dictionary to deserialize from.

Returns:

• Tool – Deserialized Tool.

toolset

Toolset

A collection of related Tools that can be used and managed as a cohesive unit.

Toolset serves two main purposes:

1. Group related tools together: Toolset allows you to organize related tools into a single collection, making it easier to manage and use them as a unit in Haystack pipelines.

   Example:

python

from typing import Annotated
from haystack.tools import tool, Toolset
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator

# Create tools with the @tool decorator (the recommended way)
@tool
def add(a: Annotated[int, "first number"], b: Annotated[int, "second number"]) -> int:
    '''Add two numbers.'''
    return a + b

@tool
def subtract(a: Annotated[int, "first number"], b: Annotated[int, "second number"]) -> int:
    '''Subtract b from a.'''
    return a - b

# Create a toolset with the math tools
math_toolset = Toolset([add, subtract])

# Use the toolset with an Agent
agent = Agent(chat_generator=OpenAIChatGenerator(), tools=math_toolset)

2. Base class for dynamic tool loading: By subclassing Toolset, you can create implementations that dynamically load tools from external sources like OpenAPI URLs, MCP servers, or other resources.

   Example:

python

from typing import Annotated
from haystack.core.serialization import generate_qualified_class_name
from haystack.tools import tool, Toolset
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator

class CalculatorToolset(Toolset):
    '''A toolset for calculator operations.'''

    def __init__(self) -> None:
        super().__init__(self._create_tools())

    def _create_tools(self):
        # These tools are defined statically for illustration purposes only.
        # In a real-world scenario, you would dynamically load tools from an external source here.
        @tool
        def add(a: Annotated[int, "first number"], b: Annotated[int, "second number"]) -> int:
            '''Add two numbers.'''
            return a + b

        @tool
        def multiply(a: Annotated[int, "first number"], b: Annotated[int, "second number"]) -> int:
            '''Multiply two numbers.'''
            return a * b

        return [add, multiply]

    def to_dict(self):
        return {
            "type": generate_qualified_class_name(type(self)),
            "data": {},  # no data to serialize as we define the tools dynamically
        }

    @classmethod
    def from_dict(cls, data):
        return cls()  # Recreate the tools dynamically during deserialization

# Create the dynamic toolset and use it with an Agent
calculator_toolset = CalculatorToolset()
agent = Agent(chat_generator=OpenAIChatGenerator(), tools=calculator_toolset)

Toolset implements the collection interface (iter, contains, len, getitem), making it behave like a list of Tools. This makes it compatible with components that expect iterable tools, such as Agent or Haystack chat generators.

When implementing a custom Toolset subclass for dynamic tool loading:

• Perform the dynamic loading in the init method
• Override to_dict() and from_dict() methods if your tools are defined dynamically
• Serialize endpoint descriptors rather than tool instances if your tools are loaded from external sources

get_selectable_tools

python

get_selectable_tools() -> list[Tool]

Return the full set of tools that can be selected by name, ignoring any active name filter.

This differs from iteration, which yields only the tools currently exposed (and respects the name filter). Override this when a Toolset's iteration does not surface every selectable tool, so name-based selection can still target the full set.

Warms up the Toolset first if needed, so lazily loaded tools (those a Toolset fetches in warm_up()) are available for selection.

Returns:

• list[Tool] – The list of tools available for name-based selection.

spawn

python

spawn() -> Toolset

Return an isolated copy of this Toolset for a single run.

The copy shares this Toolset's read-only state (its tools and any warmed-up resources) but gets fresh run-scoped state, so concurrent runs that share the same configured Toolset don't corrupt each other (for example, one run's name selection leaking into another). Warms up first if needed so the copy shares the warmed state. Subclasses with additional run-scoped state should override this.

Returns:

• Toolset – A run-scoped copy of this Toolset.

warm_up

python

warm_up() -> None

Prepare the Toolset for use.

By default, this method iterates through and warms up all tools in the Toolset. Subclasses can override this method to customize initialization behavior, such as:

• Setting up shared resources (database connections, HTTP sessions) instead of warming individual tools
• Implementing custom initialization logic for dynamically loaded tools
• Controlling when and how tools are initialized

For example, a Toolset that manages tools from an external service (like MCPToolset) might override this to initialize a shared connection rather than warming up individual tools:

python

class MCPToolset(Toolset):
    def warm_up(self) -> None:
        # Only warm up the shared MCP connection, not individual tools
        self.mcp_connection = establish_connection(self.server_url)

This method is idempotent: it only warms up the tools the first time it is called. Subclasses overriding it should preserve this contract (for example by guarding on self._is_warmed_up).

add

python

add(tool: Tool | Toolset) -> None

Add a new Tool or merge another Toolset.

If this Toolset has already been warmed up, the newly added Tool (or the tools of the added Toolset) are warmed up immediately so they are ready to use without requiring a second warm_up() call on the whole Toolset.

Note: adding a Toolset flattens it into its individual tools, so this is only recommended for Toolsets that don't manage shared resources in their warm_up() (or __init__). For example, combining with an MCPToolset, which owns a shared connection, is not recommended: the connection's lifecycle would no longer be managed by the original Toolset. In those cases combine Toolsets with + (which preserves each Toolset as a unit via _ToolsetWrapper) instead.

Parameters:

• tool (Tool | Toolset) – A Tool instance or another Toolset to add

Raises:

• ValueError – If adding the tool would result in duplicate tool names
• TypeError – If the provided object is not a Tool or Toolset

to_dict

python

to_dict() -> dict[str, Any]

Serialize the Toolset to a dictionary.

Returns:

• dict[str, Any] – A dictionary representation of the Toolset

Note for subclass implementers: The default implementation is ideal for scenarios where Tool resolution is static. However, if your subclass of Toolset dynamically resolves Tool instances from external sources—such as an MCP server, OpenAPI URL, or a local OpenAPI specification—you should consider serializing the endpoint descriptor instead of the Tool instances themselves. This strategy preserves the dynamic nature of your Toolset and minimizes the overhead associated with serializing potentially large collections of Tool objects. Moreover, by serializing the descriptor, you ensure that the deserialization process can accurately reconstruct the Tool instances, even if they have been modified or removed since the last serialization. Failing to serialize the descriptor may lead to issues where outdated or incorrect Tool configurations are loaded, potentially causing errors or unexpected behavior.

from_dict

python

from_dict(data: dict[str, Any]) -> Toolset

Deserialize a Toolset from a dictionary.

Parameters:

• data (dict[str, Any]) – Dictionary representation of the Toolset

Returns:

• Toolset – A new Toolset instance