HeyBeauty Virtual TryOn

This is a TypeScript-based MCP server that integrates virtual try-on functionality using the HeyBeauty API.

GitHub 🔗

Features

🔗 Resources for clothes with URIs and metadata
🛠️ Tools for submitting and querying try-on tasks
💬 Prompts for try-on cloth interactions
🖼️ Virtual try-on using HeyBeauty API
⚙️ Easy integration with Claude Desktop

Use Cases

E-commerce platforms looking to enhance user experience with virtual try-on capabilities
Fashion designers wanting to showcase their designs on various body types
Personal styling apps aiming to provide accurate clothing recommendations
Retail stores seeking to reduce returns by allowing customers to virtually try clothes before purchase

How to Use It

1. Apply for a HeyBeauty API Key.

2. Add the server config to your MCP Client config file:

{
  "mcpServers": {
    "heybeauty-mcp": {
      "command": "npx",
      "args": ["-y", "heybeauty-mcp"],
      "env": {
        "HEYBEAUTY_API_KEY": "your_heybeauty_api_key"
      }
    }
  }
}

3. Install dependencies:

npm install

4. Build the server:

npm run build

5. For development with auto-rebuild:

npm run watch

6. To use with Claude Desktop, add the server config:

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "heybeauty-mcp": {
      "command": "node",
      "args": ["/path/to/heybeauty-mcp/build/index.js"]
    },
    "env": {
      "HEYBEAUTY_API_KEY": "your_heybeauty_api_key"
    }
  }
}

7. Use the available tools and prompts:

submit_tryon_task: Submit a try-on task with user image URL, cloth image URL, cloth ID, and description
query_tryon_task: Query a try-on task using the task ID
tryon_cloth: Generate a structured prompt for LLM try-on

8. Access clothes resources using the cloth:// URI scheme

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FAQs

Q: What exactly is the Model Context Protocol (MCP)?

A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.

Q: How is MCP different from OpenAI's function calling or plugins?

A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.

Q: Can I use MCP with frameworks like LangChain?

A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.

Q: Why was MCP created? What problem does it solve?

A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.

Q: Is MCP secure? What are the main risks?

A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.

Q: Who is behind MCP?

A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.