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Promote unstable docs for Haystack 2.21 (#10204)
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2025-12-08 20:09:00 +01:00

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---
title: "MCPTool"
id: mcptool
slug: "/mcptool"
description: "MCPTool enables integration with external tools and services through the Model Context Protocol (MCP)."
---
# MCPTool
MCPTool enables integration with external tools and services through the Model Context Protocol (MCP).
<div className="key-value-table">
| | |
| --- | --- |
| **Mandatory init variables** | `name`: The name of the tool<br />`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
`MCPTool` is a Tool that allows Haystack to communicate with external tools and services using the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/). MCP is an open protocol that standardizes how applications provide context to LLMs, similar to how USB-C provides a standardized way to connect devices.
The `MCPTool` supports multiple transport options:
- Streamable HTTP for connecting to HTTP servers,
- SSE (Server-Sent Events) for connecting to HTTP servers **(deprecated)**,
- StdIO for direct execution of local programs.
Learn more about the MCP protocol and its architecture at the [official MCP website](https://modelcontextprotocol.io/).
### Parameters
- `name` is _mandatory_ and specifies the name of the tool.
- `server_info` is _mandatory_ and needs to be either an `SSEServerInfo`, `StreamableHttpServerInfo` or `StdioServerInfo` object that contains connection information.
- `description` is _optional_ and provides context to the LLM about what the tool does.
### Results
The Tool return results as a list of JSON objects, representing `TextContent`, `ImageContent`, or `EmbeddedResource` types from the mcp-sdk.
## Usage
Install the MCP-Haystack integration to use the `MCPTool`:
```shell
pip install mcp-haystack
```
### With Streamable HTTP Transport
You can create an `MCPTool` that connects to an external HTTP server using streamable-http transport:
```python
from haystack_integrations.tools.mcp import MCPTool, StreamableHttpServerInfo
## Create an MCP tool that connects to an HTTP server
server_info = StreamableHttpServerInfo(url="http://localhost:8000/mcp")
tool = MCPTool(name="my_tool", server_info=server_info)
## Use the tool
result = tool.invoke(param1="value1", param2="value2")
```
### With SSE Transport (deprecated)
You can create an `MCPTool` that connects to an external HTTP server using SSE transport:
```python
from haystack_integrations.tools.mcp import MCPTool, SSEServerInfo
## Create an MCP tool that connects to an HTTP server
server_info = SSEServerInfo(url="http://localhost:8000/sse")
tool = MCPTool(name="my_tool", server_info=server_info)
## Use the tool
result = tool.invoke(param1="value1", param2="value2")
```
### With StdIO Transport
You can also create an `MCPTool` that executes a local program directly and connects to it through stdio transport:
```python
from haystack_integrations.tools.mcp import MCPTool, StdioServerInfo
## Create an MCP tool that uses stdio transport
server_info = StdioServerInfo(command="uvx", args=["mcp-server-time", "--local-timezone=Europe/Berlin"])
tool = MCPTool(name="get_current_time", server_info=server_info)
## Get the current time in New York
result = tool.invoke(timezone="America/New_York")
```
### In a pipeline
You can integrate an `MCPTool` into a pipeline with a `ChatGenerator` and a `ToolInvoker`:
```python
from haystack import Pipeline
from haystack.components.converters import OutputAdapter
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.tools import ToolInvoker
from haystack.dataclasses import ChatMessage
from haystack_integrations.tools.mcp import MCPTool, StdioServerInfo
time_tool = MCPTool(
name="get_current_time",
server_info=StdioServerInfo(command="uvx", args=["mcp-server-time", "--local-timezone=Europe/Berlin"]),
)
pipeline = Pipeline()
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=[time_tool]))
pipeline.add_component("tool_invoker", ToolInvoker(tools=[time_tool]))
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 = "What is the time in New York? Be brief." # can be any city
user_input_msg = ChatMessage.from_user(text=user_input)
result = pipeline.run({"llm": {"messages": [user_input_msg]}, "adapter": {"initial_msg": [user_input_msg]}})
print(result["response_llm"]["replies"][0].text)
## The current time in New York is 1:57 PM.
```
### With the Agent Component
You can use `MCPTool` 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.components.agents import Agent
from haystack_integrations.tools.mcp import MCPTool, StdioServerInfo
time_tool = MCPTool(
name="get_current_time",
server_info=StdioServerInfo(command="uvx", args=["mcp-server-time", "--local-timezone=Europe/Berlin"]),
)
## Agent Setup
agent = Agent(
chat_generator=OpenAIChatGenerator(),
tools=[time_tool],
exit_conditions=["text"]
)
## Run the Agent
agent.warm_up()
response = agent.run(messages=[ChatMessage.from_user("What is the time in New York? Be brief.")])
## Output
print(response["messages"][-1].text)
```