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Jun 22, 2026
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docs: sync Core Integrations API reference (mcp) on Docusaurus #11704

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31 changes: 8 additions & 23 deletions docs-website/reference/integrations-api/mcp.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -787,11 +787,9 @@ Example using MCPToolset in a Haystack Pipeline:
# 1. pip install uvx mcp-server-time # Install required MCP server and tools
# 2. export OPENAI_API_KEY="your-api-key" # Set up your OpenAI API key

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

Expand All @@ -805,30 +803,18 @@ mcp_toolset = MCPToolset(
tool_names=["get_current_time"] # Only include the get_current_time tool
)

# Create a pipeline with the toolset
# Create a pipeline with an Agent that owns the tool-calling loop.
# The Agent passes the toolset to the chat generator, executes any requested
# tool calls, and continues until a final answer is produced.
pipeline = Pipeline()
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=mcp_toolset))
pipeline.add_component("tool_invoker", ToolInvoker(tools=mcp_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")
pipeline.add_component("agent", Agent(chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"), tools=mcp_toolset))

# Run the pipeline with a user question
user_input = "What is the time in New York? Be brief."
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)
result = pipeline.run({"agent": {"messages": [user_input_msg]}})
print(result["agent"]["messages"][-1].text)
```

You can also use the toolset via Streamable HTTP to talk to remote servers:
Expand Down Expand Up @@ -873,7 +859,6 @@ Example using SSE (deprecated):

```python
from haystack_integrations.tools.mcp import MCPToolset, SSEServerInfo
from haystack.components.tools import ToolInvoker

# Create the toolset with an SSE connection
sse_toolset = MCPToolset(
Expand Down Expand Up @@ -941,7 +926,7 @@ warm_up() -> None

Connect and load tools when eager_connect is turned off.

This method is automatically called by `ToolInvoker.warm_up()` and `Pipeline.warm_up()`.
This method is automatically called by `Agent.warm_up()` and `Pipeline.warm_up()`.
You can also call it directly before using the toolset to ensure all tool schemas
are available without performing a real invocation.

Expand Down
31 changes: 8 additions & 23 deletions docs-website/reference_versioned_docs/version-2.18/integrations-api/mcp.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -787,11 +787,9 @@ Example using MCPToolset in a Haystack Pipeline:
# 1. pip install uvx mcp-server-time # Install required MCP server and tools
# 2. export OPENAI_API_KEY="your-api-key" # Set up your OpenAI API key

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

Expand All @@ -805,30 +803,18 @@ mcp_toolset = MCPToolset(
tool_names=["get_current_time"] # Only include the get_current_time tool
)

# Create a pipeline with the toolset
# Create a pipeline with an Agent that owns the tool-calling loop.
# The Agent passes the toolset to the chat generator, executes any requested
# tool calls, and continues until a final answer is produced.
pipeline = Pipeline()
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=mcp_toolset))
pipeline.add_component("tool_invoker", ToolInvoker(tools=mcp_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")
pipeline.add_component("agent", Agent(chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"), tools=mcp_toolset))

# Run the pipeline with a user question
user_input = "What is the time in New York? Be brief."
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)
result = pipeline.run({"agent": {"messages": [user_input_msg]}})
print(result["agent"]["messages"][-1].text)
```

You can also use the toolset via Streamable HTTP to talk to remote servers:
Expand Down Expand Up @@ -873,7 +859,6 @@ Example using SSE (deprecated):

```python
from haystack_integrations.tools.mcp import MCPToolset, SSEServerInfo
from haystack.components.tools import ToolInvoker

# Create the toolset with an SSE connection
sse_toolset = MCPToolset(
Expand Down Expand Up @@ -941,7 +926,7 @@ warm_up() -> None

Connect and load tools when eager_connect is turned off.

This method is automatically called by `ToolInvoker.warm_up()` and `Pipeline.warm_up()`.
This method is automatically called by `Agent.warm_up()` and `Pipeline.warm_up()`.
You can also call it directly before using the toolset to ensure all tool schemas
are available without performing a real invocation.

Expand Down
31 changes: 8 additions & 23 deletions docs-website/reference_versioned_docs/version-2.19/integrations-api/mcp.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -787,11 +787,9 @@ Example using MCPToolset in a Haystack Pipeline:
# 1. pip install uvx mcp-server-time # Install required MCP server and tools
# 2. export OPENAI_API_KEY="your-api-key" # Set up your OpenAI API key

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

Expand All @@ -805,30 +803,18 @@ mcp_toolset = MCPToolset(
tool_names=["get_current_time"] # Only include the get_current_time tool
)

# Create a pipeline with the toolset
# Create a pipeline with an Agent that owns the tool-calling loop.
# The Agent passes the toolset to the chat generator, executes any requested
# tool calls, and continues until a final answer is produced.
pipeline = Pipeline()
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=mcp_toolset))
pipeline.add_component("tool_invoker", ToolInvoker(tools=mcp_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")
pipeline.add_component("agent", Agent(chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"), tools=mcp_toolset))

# Run the pipeline with a user question
user_input = "What is the time in New York? Be brief."
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)
result = pipeline.run({"agent": {"messages": [user_input_msg]}})
print(result["agent"]["messages"][-1].text)
```

You can also use the toolset via Streamable HTTP to talk to remote servers:
Expand Down Expand Up @@ -873,7 +859,6 @@ Example using SSE (deprecated):

```python
from haystack_integrations.tools.mcp import MCPToolset, SSEServerInfo
from haystack.components.tools import ToolInvoker

# Create the toolset with an SSE connection
sse_toolset = MCPToolset(
Expand Down Expand Up @@ -941,7 +926,7 @@ warm_up() -> None

Connect and load tools when eager_connect is turned off.

This method is automatically called by `ToolInvoker.warm_up()` and `Pipeline.warm_up()`.
This method is automatically called by `Agent.warm_up()` and `Pipeline.warm_up()`.
You can also call it directly before using the toolset to ensure all tool schemas
are available without performing a real invocation.

Expand Down
31 changes: 8 additions & 23 deletions docs-website/reference_versioned_docs/version-2.20/integrations-api/mcp.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -787,11 +787,9 @@ Example using MCPToolset in a Haystack Pipeline:
# 1. pip install uvx mcp-server-time # Install required MCP server and tools
# 2. export OPENAI_API_KEY="your-api-key" # Set up your OpenAI API key

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

Expand All @@ -805,30 +803,18 @@ mcp_toolset = MCPToolset(
tool_names=["get_current_time"] # Only include the get_current_time tool
)

# Create a pipeline with the toolset
# Create a pipeline with an Agent that owns the tool-calling loop.
# The Agent passes the toolset to the chat generator, executes any requested
# tool calls, and continues until a final answer is produced.
pipeline = Pipeline()
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=mcp_toolset))
pipeline.add_component("tool_invoker", ToolInvoker(tools=mcp_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")
pipeline.add_component("agent", Agent(chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"), tools=mcp_toolset))

# Run the pipeline with a user question
user_input = "What is the time in New York? Be brief."
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)
result = pipeline.run({"agent": {"messages": [user_input_msg]}})
print(result["agent"]["messages"][-1].text)
```

You can also use the toolset via Streamable HTTP to talk to remote servers:
Expand Down Expand Up @@ -873,7 +859,6 @@ Example using SSE (deprecated):

```python
from haystack_integrations.tools.mcp import MCPToolset, SSEServerInfo
from haystack.components.tools import ToolInvoker

# Create the toolset with an SSE connection
sse_toolset = MCPToolset(
Expand Down Expand Up @@ -941,7 +926,7 @@ warm_up() -> None

Connect and load tools when eager_connect is turned off.

This method is automatically called by `ToolInvoker.warm_up()` and `Pipeline.warm_up()`.
This method is automatically called by `Agent.warm_up()` and `Pipeline.warm_up()`.
You can also call it directly before using the toolset to ensure all tool schemas
are available without performing a real invocation.

Expand Down
31 changes: 8 additions & 23 deletions docs-website/reference_versioned_docs/version-2.21/integrations-api/mcp.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -787,11 +787,9 @@ Example using MCPToolset in a Haystack Pipeline:
# 1. pip install uvx mcp-server-time # Install required MCP server and tools
# 2. export OPENAI_API_KEY="your-api-key" # Set up your OpenAI API key

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

Expand All @@ -805,30 +803,18 @@ mcp_toolset = MCPToolset(
tool_names=["get_current_time"] # Only include the get_current_time tool
)

# Create a pipeline with the toolset
# Create a pipeline with an Agent that owns the tool-calling loop.
# The Agent passes the toolset to the chat generator, executes any requested
# tool calls, and continues until a final answer is produced.
pipeline = Pipeline()
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=mcp_toolset))
pipeline.add_component("tool_invoker", ToolInvoker(tools=mcp_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")
pipeline.add_component("agent", Agent(chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"), tools=mcp_toolset))

# Run the pipeline with a user question
user_input = "What is the time in New York? Be brief."
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)
result = pipeline.run({"agent": {"messages": [user_input_msg]}})
print(result["agent"]["messages"][-1].text)
```

You can also use the toolset via Streamable HTTP to talk to remote servers:
Expand Down Expand Up @@ -873,7 +859,6 @@ Example using SSE (deprecated):

```python
from haystack_integrations.tools.mcp import MCPToolset, SSEServerInfo
from haystack.components.tools import ToolInvoker

# Create the toolset with an SSE connection
sse_toolset = MCPToolset(
Expand Down Expand Up @@ -941,7 +926,7 @@ warm_up() -> None

Connect and load tools when eager_connect is turned off.

This method is automatically called by `ToolInvoker.warm_up()` and `Pipeline.warm_up()`.
This method is automatically called by `Agent.warm_up()` and `Pipeline.warm_up()`.
You can also call it directly before using the toolset to ensure all tool schemas
are available without performing a real invocation.

Expand Down
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