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* Update documentation and remove unused assets. Enhanced the 'agents' and 'components' sections with clearer descriptions and examples. Removed obsolete images and updated links for better navigation. Adjusted formatting for consistency across various documentation pages. * remove dependency * address comments * delete more empty pages * broken link * unduplicate headings * alphabetical components nav
131 lines
4.9 KiB
Plaintext
131 lines
4.9 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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| **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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:::info
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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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``` |