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SSE-based Server and mobile Angular App MCP Server

@DrBenjamin

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DrBenjamin

SSE-based Server and mobile Angular App MCP Server について

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 の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

SSE-based Server and mobile Angular App MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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)

よくある質問

1

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.

2

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.

3

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.

4

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.

5

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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