
Mandatory init variables	"server_info": Information about the MCP server to connect to
API reference	mcp
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mcp

Overview

MCPToolset is a subclass of Toolset that dynamically discovers and loads tools from any MCP-compliant server.

It supports:

SSE (Server-Sent Events): For remote MCP servers through HTTP
StdIO: For local tool execution through subprocess

The MCPToolset makes it easy to plug external tools into pipelines (with Chat Generators and ToolInvoker) or agents, with built-in support for filtering (with tool_names).

Parameters

To initialize the MCPToolset, use the following parameters:

server_info (required): Connection information for the MCP server
tool_names (optional): A list of tool names to add to the Toolset

📘
Note that if tool_names is not specified, all tools from the MCP server will be loaded. Be cautious if there are many tools (20–30+), as this can overwhelm the LLM’s tool resolution logic.

Installation

pip install mcp-haystack

Usage

With StdIO Transport

from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo

server_info = StdioServerInfo(command="uvx", args=["mcp-server-time", "--local-timezone=Europe/Berlin"])
toolset = MCPToolset(server_info=server_info, tool_names=["get_current_time"])  # If tool_names is omitted, all tools on this MCP server will be loaded (can overwhelm LLM if too many)

With SSE Transport

from haystack_integrations.tools.mcp import MCPToolset, SSEServerInfo

server_info = SSEServerInfo(base_url="http://localhost:8000")
toolset = MCPToolset(server_info=server_info, tool_names=["get_current_time"])

In a Pipeline

from haystack import Pipeline
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.tools import ToolInvoker
from haystack.components.converters import OutputAdapter
from haystack.dataclasses import ChatMessage
from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo

server_info = StdioServerInfo(command="uvx", args=["mcp-server-time", "--local-timezone=Europe/Berlin"])
toolset = MCPToolset(server_info=server_info)

pipeline = Pipeline()
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=toolset))
pipeline.add_component("tool_invoker", ToolInvoker(tools=toolset))
pipeline.add_component("adapter", OutputAdapter(
    template="{{ initial_msg + initial_tool_messages + tool_messages }}",
    output_type=list[ChatMessage],
    unsafe=True,
))
pipeline.add_component("response_llm", OpenAIChatGenerator(model="gpt-4o-mini"))

pipeline.connect("llm.replies", "tool_invoker.messages")
pipeline.connect("llm.replies", "adapter.initial_tool_messages")
pipeline.connect("tool_invoker.tool_messages", "adapter.tool_messages")
pipeline.connect("adapter.output", "response_llm.messages")

user_input = ChatMessage.from_user(text="What is the time in New York?")
result = pipeline.run({
    "llm": {"messages": [user_input]},
    "adapter": {"initial_msg": [user_input]}
})

print(result["response_llm"]["replies"][0].text)

With the Agent

from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.agents import Agent
from haystack.dataclasses import ChatMessage
from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo

toolset = MCPToolset(
    server_info=StdioServerInfo(command="uvx", args=["mcp-server-time", "--local-timezone=Europe/Berlin"]),
    tool_names=["get_current_time"]  # Omit to load all tools, but may overwhelm LLM if many
)

agent = Agent(chat_generator=OpenAIChatGenerator(), tools=toolset, exit_conditions=["text"])
agent.warm_up()

response = agent.run(messages=[ChatMessage.from_user("What is the time in New York?")])
print(response["messages"][-1].text)