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133 lines
5.1 KiB
Plaintext
133 lines
5.1 KiB
Plaintext
---
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title: "MCPToolset"
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id: mcptoolset
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slug: "/mcptoolset"
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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`."
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---
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# MCPToolset
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`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`.
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| | |
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| ---------------------------- | ------------------------------------------------------------------------------------- |
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| **Mandatory init variables** | "server_info": Information about the MCP server to connect to |
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| **API reference** | [mcp](/reference/integrations-mcp) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mcp |
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## Overview
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MCPToolset is a subclass of `Toolset` that dynamically discovers and loads tools from any MCP-compliant server.
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It supports:
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- **Streamable HTTP** for connecting to HTTP servers
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- **SSE (Server-Sent Events)** _(deprecated)_ for remote MCP servers through HTTP
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- **StdIO** for local tool execution through subprocess
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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`).
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### Parameters
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To initialize the MCPToolset, use the following parameters:
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- `server_info` (required): Connection information for the MCP server
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- `tool_names` (optional): A list of tool names to add to the Toolset
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:::note
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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.
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:::
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### Installation
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```shell
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pip install mcp-haystack
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```
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## Usage
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### With StdIO Transport
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```python
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from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo
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server_info = StdioServerInfo(command="uvx", args=["mcp-server-time", "--local-timezone=Europe/Berlin"])
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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)
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```
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### With Streamable HTTP Transport
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```python
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from haystack_integrations.tools.mcp import MCPToolset, StreamableHttpServerInfo
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server_info = SSEServerInfo(url="http://localhost:8000/mcp")
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toolset = MCPToolset(server_info=server_info, tool_names=["get_current_time"])
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```
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### With SSE Transport (deprecated)
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```python
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from haystack_integrations.tools.mcp import MCPToolset, SSEServerInfo
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server_info = SSEServerInfo(url="http://localhost:8000/sse")
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toolset = MCPToolset(server_info=server_info, tool_names=["get_current_time"])
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```
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### In a Pipeline
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```python
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from haystack import Pipeline
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from haystack.components.generators.chat import OpenAIChatGenerator
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from haystack.components.tools import ToolInvoker
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from haystack.components.converters import OutputAdapter
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from haystack.dataclasses import ChatMessage
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from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo
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server_info = StdioServerInfo(command="uvx", args=["mcp-server-time", "--local-timezone=Europe/Berlin"])
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toolset = MCPToolset(server_info=server_info)
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pipeline = Pipeline()
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pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=toolset))
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pipeline.add_component("tool_invoker", ToolInvoker(tools=toolset))
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pipeline.add_component("adapter", OutputAdapter(
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template="{{ initial_msg + initial_tool_messages + tool_messages }}",
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output_type=list[ChatMessage],
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unsafe=True,
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))
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pipeline.add_component("response_llm", OpenAIChatGenerator(model="gpt-4o-mini"))
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pipeline.connect("llm.replies", "tool_invoker.messages")
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pipeline.connect("llm.replies", "adapter.initial_tool_messages")
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pipeline.connect("tool_invoker.tool_messages", "adapter.tool_messages")
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pipeline.connect("adapter.output", "response_llm.messages")
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user_input = ChatMessage.from_user(text="What is the time in New York?")
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result = pipeline.run({
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"llm": {"messages": [user_input]},
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"adapter": {"initial_msg": [user_input]}
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})
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print(result["response_llm"]["replies"][0].text)
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```
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### With the Agent
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```python
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from haystack.components.generators.chat import OpenAIChatGenerator
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from haystack.components.agents import Agent
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from haystack.dataclasses import ChatMessage
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from haystack_integrations.tools.mcp import MCPToolset, StdioServerInfo
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toolset = MCPToolset(
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server_info=StdioServerInfo(command="uvx", args=["mcp-server-time", "--local-timezone=Europe/Berlin"]),
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tool_names=["get_current_time"] # Omit to load all tools, but may overwhelm LLM if many
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)
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agent = Agent(chat_generator=OpenAIChatGenerator(), tools=toolset, exit_conditions=["text"])
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agent.warm_up()
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response = agent.run(messages=[ChatMessage.from_user("What is the time in New York?")])
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print(response["messages"][-1].text)
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```
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