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MCP (Model Context Protocol) is a protocol for exposing tools and resources to AI agents. Read more about MCP here.

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Basic MCP Usage

The ready-to-run basic MCP usage example is available here!
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MCP Configuration

Configure MCP servers using a dictionary with server names and connection details following this configuration format
mcp_config
mcp_config = {
    "mcpServers": {
        "fetch": {
            "command": "uvx",
            "args": ["mcp-server-fetch"]
        },
        "repomix": {
            "command": "npx",
            "args": ["-y", "repomix@1.4.2", "--mcp"]
        },
    }
}
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Tool Filtering

Use filter_tools_regex to control which MCP tools are available to the agent
filter_tools_regex
agent = Agent(
    llm=llm,
    tools=tools,
    mcp_config=mcp_config,
    filter_tools_regex="^(?!repomix)(.*)|^repomix.*pack_codebase.*$",
)

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MCP with OAuth

The ready-to-run MCP with OAuth example is available here!
For MCP servers requiring OAuth authentication:
  • Configure OAuth-enabled MCP servers by specifying the URL and auth type
  • The SDK automatically handles the OAuth flow when first connecting
  • When the agent first attempts to use an OAuth-protected MCP server’s tools, the SDK initiates the OAuth flow via FastMCP
  • User will be prompted to authenticate
  • Access tokens are securely stored and automatically refreshed by FastMCP as needed
mcp_config
mcp_config = {
    "mcpServers": {
        "Notion": {
            "url": "https://mcp.notion.com/mcp",
            "auth": "oauth"
        }
    }
}

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Ready-to-Run Basic MCP Usage Example

This example is available on GitHub: examples/01_standalone_sdk/07_mcp_integration.py
Here’s an example integrating MCP servers with an agent:
examples/01_standalone_sdk/07_mcp_integration.py
import os

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Agent,
    Conversation,
    Event,
    LLMConvertibleEvent,
    get_logger,
)
from openhands.sdk.security.llm_analyzer import LLMSecurityAnalyzer
from openhands.sdk.tool import Tool
from openhands.tools.file_editor import FileEditorTool
from openhands.tools.terminal import TerminalTool


logger = get_logger(__name__)

# Configure LLM
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")
llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

cwd = os.getcwd()
tools = [
    Tool(name=TerminalTool.name),
    Tool(name=FileEditorTool.name),
]

# Add MCP Tools
mcp_config = {
    "mcpServers": {
        "fetch": {"command": "uvx", "args": ["mcp-server-fetch"]},
        "repomix": {"command": "npx", "args": ["-y", "repomix@1.4.2", "--mcp"]},
    }
}
# Agent
agent = Agent(
    llm=llm,
    tools=tools,
    mcp_config=mcp_config,
    # This regex filters out all repomix tools except pack_codebase
    filter_tools_regex="^(?!repomix)(.*)|^repomix.*pack_codebase.*$",
)

llm_messages = []  # collect raw LLM messages


def conversation_callback(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        llm_messages.append(event.to_llm_message())


# Conversation
conversation = Conversation(
    agent=agent,
    callbacks=[conversation_callback],
    workspace=cwd,
)
conversation.set_security_analyzer(LLMSecurityAnalyzer())

logger.info("Starting conversation with MCP integration...")
conversation.send_message(
    "Read https://github.com/OpenHands/OpenHands and write 3 facts "
    "about the project into FACTS.txt."
)
conversation.run()

conversation.send_message("Great! Now delete that file.")
conversation.run()

print("=" * 100)
print("Conversation finished. Got the following LLM messages:")
for i, message in enumerate(llm_messages):
    print(f"Message {i}: {str(message)[:200]}")

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")
You can run the example code as-is.
The model name should follow the LiteLLM convention: provider/model_name (e.g., anthropic/claude-sonnet-4-5-20250929, openai/gpt-4o). The LLM_API_KEY should be the API key for your chosen provider.
ChatGPT Plus/Pro subscribers: You can use LLM.subscription_login() to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the LLM Subscriptions guide for details.

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Ready-to-Run MCP with OAuth Example

This example is available on GitHub: examples/01_standalone_sdk/08_mcp_with_oauth.py
examples/01_standalone_sdk/08_mcp_with_oauth.py
import os

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Agent,
    Conversation,
    Event,
    LLMConvertibleEvent,
    get_logger,
)
from openhands.sdk.tool import Tool
from openhands.tools.file_editor import FileEditorTool
from openhands.tools.terminal import TerminalTool


logger = get_logger(__name__)

# Configure LLM
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")
llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

cwd = os.getcwd()
tools = [
    Tool(
        name=TerminalTool.name,
    ),
    Tool(name=FileEditorTool.name),
]

mcp_config = {
    "mcpServers": {"Notion": {"url": "https://mcp.notion.com/mcp", "auth": "oauth"}}
}
agent = Agent(llm=llm, tools=tools, mcp_config=mcp_config)

llm_messages = []  # collect raw LLM messages


def conversation_callback(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        llm_messages.append(event.to_llm_message())


# Conversation
conversation = Conversation(
    agent=agent,
    callbacks=[conversation_callback],
)

logger.info("Starting conversation with MCP integration...")
conversation.send_message("Can you search about OpenHands V1 in my notion workspace?")
conversation.run()

print("=" * 100)
print("Conversation finished. Got the following LLM messages:")
for i, message in enumerate(llm_messages):
    print(f"Message {i}: {str(message)[:200]}")
You can run the example code as-is.
The model name should follow the LiteLLM convention: provider/model_name (e.g., anthropic/claude-sonnet-4-5-20250929, openai/gpt-4o). The LLM_API_KEY should be the API key for your chosen provider.
ChatGPT Plus/Pro subscribers: You can use LLM.subscription_login() to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the LLM Subscriptions guide for details.

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Next Steps