Omnimcp

OmniMCP is a project that provides rich UI context and interaction capabilities for AI models through the Model Context Protocol (MCP) and OmniParser. It supports functions such as visual perception, LLM planning, and action execution, enabling in - depth understanding and precise interaction with the user interface.

Operating system automation Developer tools #UI interaction #Visual analysis #Intelligent planning #Automated control .Python

rating : 2.5 points

downloads : 42

update time : 2025-04-23

What is the MCP server?

The MCP server is a tool specifically designed to handle UI element parsing and interaction. It can help AI models better understand and operate the user interface to complete complex tasks.

How to use the MCP server?

The MCP server can be quickly deployed and run through simple configuration and command-line tools. Users can easily set target tasks and let the AI model automatically perform the required operations.

Applicable scenarios

The MCP server is very suitable for scenarios that require highly automated and intelligent operations, such as software testing, customer service robots, data analysis, etc.

Main features

Visual perceptionIdentify components such as buttons and input boxes on the screen by parsing UI elements.

LLM planningUse large language models to generate the next operation plan.

Interaction executionImplement UI operations through mouse and keyboard simulation.

Automatic deploymentSupport one-click deployment to AWS EC2 instances and have an automatic shutdown function.

Advantages and limitations

Advantages

Improve AI's understanding of the UI

Reduce manual intervention and improve efficiency

Support multiple operating systems and devices

Have automated management and cleaning functions

Limitations

Require certain system environment support

Initial deployment may take some time

Some advanced functions may be affected by network latency

How to use

Install the MCP server

Clone the project code and install dependencies.

Configure environment variables

Edit the.env file and fill in necessary information such as AWS credentials.

Start the server

Run the command to start the MCP server.

Usage examples

Case 1: Calculator taskOpen the calculator and calculate 5 multiplied by 9.

Case 2: Login formSimulate filling in the username and password and submit the form.

Frequently Asked Questions

Does the MCP server support multi - language interfaces?

How to stop a deployed AWS instance?

Does the MCP server require specific hardware configuration?

Related resources

Official documentation

Get more information and tutorials about the MCP server.

Source code repository

View and download the latest version of the source code.

Example video

Watch the actual demonstration of the MCP server.

🚀 OmniMCP Project Documentation

OmniMCP is a comprehensive project aiming to achieve multi - modal understanding and intelligent operations in human - machine interaction. This project combines natural language processing, computer vision, and robotics to enable end - to - end control of complex tasks.

🚀 Quick Start

OmniMCP integrates multiple sensor data for real - time analysis, supports multi - language dialogue, and can execute complex tasks through robotic arms or virtual assistants.

Installation

Clone the project repository:

git clone https://github.com/OpenAdaptAI/OmniMCP.git
cd OmniMCP

Install dependencies:
```
pip install -r requirements.txt
```
Start the service:
```
python main.py
```

Usage

Configuration file: Edit config.json to configure sensors and operating devices.
Start the service: Run the main program and access the Web interface for control.

✨ Features

OmniMCP Architecture: Integrate multiple sensor data (such as images and voices) for real - time analysis.
Natural Language Interaction: Support multi - language dialogue, understand context and generate corresponding responses.
Intelligent Operations: Execute complex tasks through robotic arms or virtual assistants.

🔧 Technical Details

1. System Architecture

Sensor Integration: Integrate cameras, microphones, and other IoT devices.
Data Processing: Use deep learning models to perform real - time analysis on multi - modal data.
Decision Engine: Generate operation instructions based on the analysis results.

2. Natural Language Processing

Multi - language Support: Support voice recognition and text understanding in multiple languages.
Context Understanding: Provide more intelligent answers through context reasoning.

3. Intelligent Operations

Robotic Arm Control: Achieve precise control of industrial robotic arms.
Virtual Assistant Integration: Interact with mainstream virtual assistants (such as ChatGPT).

💻 Usage Examples

Basic Usage

# Initialize an OmniMCP instance
omni = OmniMCP()

# Receive voice commands
command = omni.stt.listen()
print("User command:", command)

# Analyze image data
image_path = "input.jpg"
objects = omni.vision.analyze(image_path)
print("Detected objects:", objects)

# Execute operations
omni.robot.control("grab", object_id=0)

📚 Documentation

Currently, OmniMCP has achieved basic multi - modal data processing and simple task operation functions. It is being developed with more complex scene understanding capabilities and high - precision operation algorithms.

🤝 Contribution Guidelines

Issue Reporting: Submit bugs or feature requests through GitHub Issues.
Code Contribution: Fork the repository and submit a PR.
Documentation Improvement: Provide usage feedback and optimization suggestions.