MCP Assistant Playground

MCP Assistant Playground

An intelligent chatbot based on Streamlit that uses GPT-4o to automatically route user requests to different tools (such as chatting, image generation, database queries, voice synthesis, etc.), supporting rapid experimentation with AI tool routing functions.

Artificial intelligence chatbots Developer tools #Intelligent Routing #AI Tools #Chatbot #Voice Synthesis .Python

rating : 2 points

downloads : 7.7K

update time : 2025-04-29

What is the MCP Assistant Player?

The MCP Assistant Player is a chatbot interface powered by Streamlit. It uses OpenAI GPT-4o to intelligently route user input to custom tools, such as text conversations, image generation, database queries, and text-to-speech functions. This platform aims to quickly experiment with AI-driven tool routing, inspired by the Claude-style confirmation process.

How to use the MCP Assistant Player?

First, you need to set up an environment variable file (.env), then install the dependencies and run the application. Once launched, you can access the interface through a browser and start interacting with the chatbot.

Applicable Scenarios

Suitable for application developers who need to integrate multiple AI functions, researchers, and individual users who want to explore the possibilities of AI tool combinations.

Main Features

Natural Language Tool Selection

Analyze user input through GPT-4o and automatically assign it to the appropriate tool.

Image Generation

Generate high-quality images in real-time using the OpenAI DALL·E 3 model.

Text-to-Speech

Synthesize audio using the GPT-4o micro TTS module.

Supabase Integration

Support CRUD operations on the Supabase database for easy management of member data.

Advantages

High flexibility, allowing new tools to be easily added.

Intuitive user interface design.

Powerful combination of AI-driven functions.

Open source with a free trial version available.

Limitations

Requires some knowledge of Python programming.

Some advanced features may depend on paid API keys.

The processing speed may slow down for very large datasets.

How to Use

Clone the Project Repository

Clone the source code of the MCP Assistant Player from GitHub.

Set Up a Virtual Environment

Create and activate a Python virtual environment.

Install Dependencies

Ensure that all necessary Python packages are installed.

Configure Environment Variables

Create a file named .env in the root directory and fill in your API keys and other necessary information.

Start the Application

Run the launch.py script to start the service.

Usage Examples

Generate a Landscape Image

Request to generate an image depicting mountains and lakes.

Query Member Information

Query specific member records in the Supabase database.

Text-to-Speech

Convert a piece of text into speech output.

Frequently Asked Questions

How do I start my first session?

Does it support multiple languages?

Why do my requests sometimes fail?

Related Resources

Official Documentation

Visit the GitHub homepage to get more information about the MCP Assistant Player.

Example Code

View some practical examples to understand how to use this tool.

Video Tutorial

Watch a short video to learn how to get started quickly.

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