---
title: "MCPToolset"
id: mcptoolset
slug: "/mcptoolset"
description: "`MCPToolset` connects to an MCP-compliant server and automatically loads all available tools into a single manageable unit. These tools can be used directly with components like Chat Generator, `ToolInvoker`, or `Agent`."
---

# MCPToolset

`MCPToolset` connects to an MCP-compliant server and automatically loads all available tools into a single manageable unit. These tools can be used directly with components like Chat Generator, `ToolInvoker`, or `Agent`.

<div className="key-value-table">

|                              |                                                                                       |
| ---------------------------- | ------------------------------------------------------------------------------------- |
| **Mandatory init variables** | `server_info`: Information about the MCP server to connect to                         |
| **API reference**            | [mcp](/reference/integrations-mcp)                                                           |
| **GitHub link**              | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mcp |

</div>

## Overview

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

It supports:

- **Streamable HTTP** for connecting to HTTP servers
- **SSE (Server-Sent Events)** _(deprecated)_ 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
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

```shell
pip install mcp-haystack
```

## Usage

### With StdIO Transport

```python
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 Streamable HTTP Transport

```python
from haystack_integrations.tools.mcp import MCPToolset, StreamableHttpServerInfo

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

### With SSE Transport (deprecated)

```python
from haystack_integrations.tools.mcp import MCPToolset, SSEServerInfo

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

### In a Pipeline

```python
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

```python
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)
```
