BenBox MCP Server

@DrBenjamin

Repository

Basic information

About BenBox MCP Server

Agent AI app utilizing MCP tools with Angular mobile and Phoenix desktop app.

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

BenBox MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

BenBox MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is BenBox?

BenBox is an Agent AI application that uses Model Context Protocol (MCP) tools, integrating with Ollama, Azure OpenAI, Snowflake, PostgreSQL, and MinIO. It provides an Angular mobile app and a Phoenix desktop app, targeting developers building cloud-native AI agents for image recognition and file organization.

How to use BenBox?

Clone the repository, create a conda environment from environment.yml, install the mcp package, and run the MCP server (python src/server.py or mcp dev src/server.py). Launch the Streamlit app with streamlit run app.py, the Angular mobile app with ng serve, or the desktop app with python BenBox.py. Configure secrets in .streamlit/st.secrets.toml (e.g., MCP_URL, AZURE_OPENAI_API_KEY, Ollama settings).

Key features of BenBox

SSE‑based MCP server for decoupled agent communication.
Supports Ollama and Azure OpenAI as LLM providers.
Image recognition via Streamlit upload or MCP Inspector.
Angular mobile app with Capacitor for iOS.
Desktop app (PyInstaller) for file organization.
Docker support for containerized deployment.
Integration with Snowflake, PostgreSQL, and MinIO storage.

Use cases of BenBox

Build an AI agent that recognizes images using a local or cloud LLM.
Run a mobile image‑upload interface connected to an MCP server.
Organize and manage files with a desktop GUI backed by MinIO.
Deploy a cloud‑native agent with decoupled MCP client and server.

Frequently asked questions

What runtime dependencies does BenBox require?

BenBox requires Python 3, Node.js, npm, conda, Ollama (for local models), and optionally Azure CLI for Azure OpenAI.

Where does data persist in BenBox?

User data can be stored in Snowflake or PostgreSQL, and file storage uses MinIO buckets. Configuration is set via `.streamlit/st.secrets.toml`.

How does the MCP server communicate?

The MCP server runs on HTTP (SSE) — configurable via `MCP_URL` in secrets, defaulting to `http://127.0.0.1:8080`. Clients connect over this transport.

How is authentication handled?

Azure OpenAI requires `az login` (or `az login --use-device-code`). MinIO uses access/secret keys. No built‑in auth for the MCP server itself is documented.

Can BenBox run in a container?

Yes, Dockerfiles are provided for the MCP server (`Dockerfile_MCP`), Streamlit app, Angular app, and the desktop VNC container. Use `docker-compose up -d` to start all services.

Comments

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