MCP Hub Project

Overview

The MCP (Model Context Protocol) Hub is a sophisticated research assistant built using Gradio's MCP server functionality. This project demonstrates how to build a workflow of connected AI agents that work together to provide deep research capabilities.

The system orchestrates a 5-step deep research workflow:

1. Question Enhancement: Breaks down a user's original query into five distinct sub-questions
2. Web Search: Conducts web searches for each sub-question using Tavily API
3. LLM Summarization: Summarizes search results for each sub-question using Nebius LLMs
4. Citation Formatting: Extracts and formats citations from web search results
5. Result Combination: Merges all summaries into a comprehensive final answer

Features

• MCP Server Implementation: Built on Gradio's MCP server capabilities for seamless agent communication
• Multi-Agent Architecture: Demonstrates how to build interconnected agent services
• Real-time Web Search: Integration with Tavily API for up-to-date information
• LLM Processing: Uses Nebius (OpenAI-compatible) models for text processing
• Structured Workflow: Showcases a sophisticated multi-step AI research process
• Citation Generation: Automatically formats APA-style citations from web sources

Prerequisites

• Python 3.12+
• API keys for:
  • Nebius API
  • Tavily API

Installation

1. Clone this repository
2. Create a virtual environment (recommended)

python -m venv venv
# Activate the virtual environment:
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt
# or use the pyproject.toml with your preferred Python package manager

4. Create a .env file with the following content:

NEBIUS_API_KEY=nb-...
TAVILY_API_KEY=tvly-...

Usage

Run the main application:

python main.py

This will launch the Gradio interface at http://127.0.0.1:7860/

The MCP schema will be available at http://127.0.0.1:7860/gradio_api/mcp/schema

Available Agents

The project includes several agent services:

1. Question Enhancer: Splits a request into five sub-questions using Qwen3-32B-fast
2. Web Search Agent: Performs web searches via Tavily API
3. LLM Processor: Processes text with Nebius LLMs for summarization, reasoning, or keyword extraction
4. Citation Formatter: Extracts URLs and formats them as APA-style citations
5. Orchestrator: Coordinates all agents in a cohesive workflow

Tutorial Scripts

The tutorial_scripts/ directory contains example Gradio applications for learning:

• simple_app.py: A basic Gradio interface
• letter_count.py: A simple letter counting MCP server
• And more examples demonstrating various Gradio and MCP features

MCP Implementation Details

This project demonstrates how to:

• Create MCP-compatible function definitions with proper typing and docstrings
• Launch a Gradio app as an MCP server (mcp_server=True)
• Structure a multi-agent workflow
• Pass data between agents in a structured format

License

[Your license information here]

Contributing

[Your contribution guidelines here]

Acknowledgments

• Gradio for providing the MCP server functionality
• Nebius for LLM capabilities
• Tavily for web search capabilities