Browser Automation 🌐 - Navigate webpages, click elements, input text, and more
Google Search 🔍 - Execute searches and retrieve result links
Python Code Execution 🐍 - Run Python code in a secure environment
File Saving 💾 - Save content to local files
Termination Control 🛑 - Control program execution flow

🚀 Installation

Prerequisites

Python 3.10+
OpenManus project dependencies

Installation Steps

First, install the OpenManus project:

git clone https://github.com/mannaandpoem/OpenManus.git
cd OpenManus

Install dependencies:

# Using uv (recommended)
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv
source .venv/bin/activate  # Unix/macOS
# or .venv\Scripts\activate  # Windows
uv sync

Install MCP dependencies:

pip install mcp-python

📖 Usage

Starting the MCP Server

The server supports two communication modes: stdio and HTTP.

stdio mode (default)

python mcp_server.py

HTTP mode

python mcp_server.py --transport http --host 127.0.0.1 --port 8000

Command Line Arguments

--transport: Communication method, choose "stdio" or "http" (default: stdio)
--host: HTTP server host address (default: 127.0.0.1)
--port: HTTP server port (default: 8000)

💻 Client Example

Check out mcp_client_example.py to learn how to connect to the server and call tools using the MCP client.

Running the Client Example

First, start the server in HTTP mode:

python mcp_server.py --transport http

In another terminal, run the client example:

python mcp_client_example.py

🤖 LLM Integration

The MCP server can be integrated with LLMs that support tool calling, such as Claude 3 Opus/Sonnet/Haiku.

Example with Claude

import anthropic
from mcp.client import MCPClient

# Initialize Claude client
client = anthropic.Anthropic(api_key="your_api_key")

# Connect to MCP server
mcp_client = await MCPClient.create_http("http://localhost:8000")

# Get tool definitions
tools = await mcp_client.list_tools()
tool_definitions = [tool.to_dict() for tool in tools]

# Create Claude message
message = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1000,
    temperature=0,
    system="You are a helpful assistant that can use tools to help users.",
    messages=[{"role": "user", "content": "Search for Model Context Protocol and summarize the top 3 results"}],
    tools=tool_definitions
)

# Handle tool calls
for tool_call in message.content:
    if hasattr(tool_call, "tool_use"):
        tool_name = tool_call.tool_use.name
        tool_params = tool_call.tool_use.input
        
        # Call MCP tool
        result = await mcp_client.invoke_tool(tool_name, tool_params)
        
        # Send results back to Claude
        message = client.messages.create(
            model="claude-3-opus-20240229",
            max_tokens=1000,
            temperature=0,
            system="You are a helpful assistant that can use tools to help users.",
            messages=[
                {"role": "user", "content": "Search for Model Context Protocol and summarize the top 3 results"},
                {"role": "assistant", "content": [tool_call]},
                {"role": "user", "content": [{"type": "tool_result", "tool_use_id": tool_call.tool_use.id, "content": result}]}
            ],
            tools=tool_definitions
        )

🔒 Security Considerations

By default, the HTTP server only listens on localhost (127.0.0.1) and is not exposed externally
When using in production, ensure proper authentication and authorization mechanisms are in place
The Python execution tool has timeout limits to prevent long-running code

📄 License

Same license as the OpenManus project