Next Gen UI MCP Server Container

Next Gen UI MCP Server container image.

For more information visit GitHub.

podman pull quay.io/next-gen-ui/mcp

Usage

podman run --rm -it -p 5100:5100 --env MCP_PORT="5100" \
       --env NGUI_MODEL="llama3.2" --env NGUI_PROVIDER_API_BASE_URL=http://host.containers.internal:11434 --env NGUI_PROVIDER_API_KEY="ollama" \
       quay.io/next-gen-ui/mcp

Configuration

The MCP server container can be configured via environment variables. All configuration options available to the standalone server are supported.

Environment Variables Reference

Environment Variable	Default Value	Description
`MCP_TRANSPORT`	`streamable-http`	Transport protocol (`stdio`, `sse`, `streamable-http`)
`MCP_HOST`	`0.0.0.0`	Host to bind to (for HTTP transports)
`MCP_PORT`	`5000`	Port to bind to (for HTTP transports)
`NGUI_COMPONENT_SYSTEM`	`json`	Component system (`json`, `rhds`)
`NGUI_PROVIDER`	`langchain`	Inference provider (`mcp`, `llamastack`, `langchain`)
`NGUI_MODEL`	`gpt-4o`	Model name (required for non-MCP providers)
`NGUI_PROVIDER_API_BASE_URL`	-	Base URL for OpenAI-compatible API
`NGUI_PROVIDER_API_KEY`	-	API key for the LLM provider
`NGUI_PROVIDER_LLAMA_URL`	-	LlamaStack server URL (if `llamastack` is used)

Providers

The Next Gen UI MCP server supports three inference providers, controlled by the NGUI_PROVIDER environment variable:

Selects the inference provider to use for generating UI components:

Provider `mcp`

Uses Model Context Protocol sampling to leverage the client's LLM capabilities. No additional configuration required as it uses the connected MCP client's model.

Provider `langchain`:

Uses LangChain with OpenAI-compatible APIs.

Requires:

NGUI_MODEL: Model name (e.g., gpt-4o, llama3.2)
NGUI_PROVIDER_API_KEY: API key for the provider
NGUI_PROVIDER_API_BASE_URL (optional): Custom base URL for OpenAI-compatible APIs like Ollama

Examples:

OpenAI: https://api.openai.com/v1 (default)
Ollama: http://host.containers.internal:11434/v1

Provider `llamastack`:

Uses LlamaStack server for inference.

Requires:

NGUI_MODEL: Model name available on the LlamaStack server
NGUI_PROVIDER_LLAMA_URL: URL of the LlamaStack server

Usage Examples

Basic Usage with Ollama (Local LLM)

podman run --rm -it -p 5000:5000 \
    --env MCP_PORT="5000" \
    --env NGUI_MODEL="llama3.2" \
    --env NGUI_PROVIDER_API_BASE_URL="http://host.containers.internal:11434/v1" \
    --env NGUI_PROVIDER_API_KEY="ollama" \
    quay.io/next-gen-ui/mcp

OpenAI Configuration

podman run --rm -it -p 5000:5000 \
    --env NGUI_MODEL="gpt-4o" \
    --env NGUI_PROVIDER_API_KEY="your-openai-api-key" \
    quay.io/next-gen-ui/mcp

Using with Environment File

Create a .env file:

# .env file
MCP_PORT=5000
MCP_HOST=0.0.0.0
MCP_TRANSPORT=streamable-http
NGUI_COMPONENT_SYSTEM=json
NGUI_PROVIDER=langchain
NGUI_MODEL=gpt-4o
NGUI_PROVIDER_API_KEY=your-api-key-here

Run with environment file:

podman run --rm -it -p 5000:5000 --env-file .env quay.io/next-gen-ui/mcp

LlamaStack Provider

podman run --rm -it -p 5000:5000 \
    --env NGUI_PROVIDER="llamastack" \
    --env NGUI_MODEL="llama3.2-3b" \
    --env NGUI_PROVIDER_LLAMA_URL="http://host.containers.internal:5001" \
    quay.io/next-gen-ui/mcp

Network Configuration

For local development connecting to services running on the host machine:

Use host.containers.internal to access host services (works with Podman and Docker Desktop)
For Linux with Podman, you may need to use host.docker.internal or the host's IP address
Ensure the target services (like Ollama) are accessible from containers