mcp-sentiment-analysis

sentiment analysis application

We’ll build a sentiment analysis application that consists of three main parts: the server, the client, and the deployment.

Server Side:

• Uses Gradio to create a web interface and MCP server via gr.Interface
• Implement a sentiment analysis tool using TextBlob
• Exposes the tool through HTTP and MCP protocols

Client Side:

• Implements a HuggingFace.js client
• Or, Creates a smolagents Python Client
• Demonstrates how to use the same server with different client implementations

Deployment:

• Deploys the server to Hugging Face Spaces
• Configures the clients to work with the deployed server

Building the Gradio MCP Server

• create our sentiment analysis MCP server using Gradio. This server will expose a sentiment analysis tool that can be used by both human users through a web interface and AI models through the MCP protocol.

Introduction to Gradio MCP Integration

Gradio provides a straightforward way to create MCP servers by automatically converting your Python functions into MCP tools. When you set mcp_server=True in launch(), Gradio:

• Automatically converts your functions into MCP Tools
• Maps input components to tool argument schemas
• Determines response formats from output components
• Sets up JSON-RPC over HTTP+SSE for client-server communication
• Creates both a web interface and an MCP server endpoint