SSE-based Server and mobile Angular App MCP Server

@DrBenjamin

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DrBenjamin

关于 SSE-based Server and mobile Angular App MCP Server

MCP server for image recognition with Angular mobile client app.

#python #angular #js #ts#image-recognition#streamlit #mcp-server

SSE-based Server and mobile Angular App MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "imagerecog": {
      "command": "python",
      "args": [
        "-m",
        "pip",
        "install",
        "mcp[cli]"
      ]
    }
  }
}

SSE-based Server and mobile Angular App MCP Server 工具

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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is SSE-based Server and mobile Angular App?

An image recognition tool built on the Model Context Protocol (MCP) using an SSE-based server–client architecture. It enables decoupled, cloud‑native image recognition where the MCP server runs as a separate process that agents can connect to and disconnect from on demand, unlike the traditional STDIO‑based pattern.

How to use SSE-based Server and mobile Angular App?

Install Node.js, Python, and the MCP package, then clone the repository. Run the MCP server with python src/server.py (or mcp dev src/server.py for development) and launch either the Streamlit client (python -m streamlit run mobile/app.py) or the mobile Angular app (ng serve after installing dependencies). Configure the OpenAI or Ollama API key in .streamlit/st.secrets.toml.

Key features of SSE-based Server and mobile Angular App

SSE‑based MCP server for cloud‑native deployments
Image recognition using OpenAI or local Ollama models
Streamlit client app for desktop interaction
Mobile Angular app built with Capacitor for iOS
Docker support for hosting the MCP server
Configurable system and user prompts for recognition

Use cases of SSE-based Server and mobile Angular App

Recognize images via an always‑running MCP server accessible from remote clients
Integrate image recognition into VS Code Copilot Chat or MCP Inspector
Run a cloud‑hosted MCP server with a mobile companion app
Experiment with different vision models (Ollama local or OpenAI API)

常见问题

What distinguishes this from a STDIO‑based MCP server?

SSE allows the MCP server and client to run as decoupled processes, potentially on separate nodes, fitting cloud‑native patterns better than STDIO where the client spawns a subprocess.

What dependencies are required?

Node.js, Python, the MCP package (via pip or conda), and either Ollama (for local models) or an OpenAI API key. For mobile, Angular CLI and Capacitor are needed.

How is authentication handled?

Authentication is not built‑in; the OpenAI API key is provided via the `.streamlit/st.secrets.toml` configuration file for cloud‑based recognition.

Can I deploy the server in a container?

Yes, a Dockerfile is provided. Build and push the image, then add it to a client like VS Code or Claude Desktop.

Where does image data reside?

Images are sent as base64 bytes or URLs from the client to the MCP server; the server passes them to the chosen LLM (local Ollama or OpenAI) for recognition. No image storage is described.

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