MCP: Multi-Agent Control Point

Integrations

Enables containerized deployment of the MCP server with Docker and Docker Compose, allowing for easier setup and distribution across machines
Powers the backend API server that handles routing user questions to appropriate specialized agents
Integrates with Ollama to access the deepseek-r1:7b language model for expert responses when specialized agents cannot handle a query

🧠 MCP: Multi-Agent Control Point

This project implements a multi-agent server that routes user queries to an LLM model or specialized agents (such as date, location, weather, or a technical expert). It includes a simple web interface built with Streamlit for ease of use.

🚀 Features

🌐 Backend with FastAPI
🧠 Specialized agents (date, location, weather, LLM expert)
🖥️ Visual interface with Streamlit (GUI)
🐳 Docker containers for easy deployment
🔌 Client-server communication ready for local or remote network

📁 Project structure

MCP/
├── core/
│   ├── registry.py          # Registra todos los agentes
│   └── router_llm.py        # Permite distribución entre agentes
├── agents/
│   └── agent.py             # cada agente incluido en el servidor
├── server/
│   ├── mcp_server.py        # Lógica del MCP
│   └── api.py               # Backend FastAPI
├── gui/
│   ├── app.py               # Interfaz Streamlit
│   └── .streamlit/
│       └── secrets.toml     # Configuración del backend
├── utils/
│   └── json_parser.py       # Función para dividir json
├── requirements.txt         # Dependencias comunes
├── Dockerfile.backend       # Imagen del backend
├── Dockerfile.frontend      # Imagen del frontend
└── docker-compose.yml       # Orquestación de servicios

⚙️ Requirements

🧪 Quick installation

1. Clone the repository

git clone https://github.com/tu-usuario/MCP.git
cd MCP

2. Create configuration file for Streamlit

Inside the gui directory, create the file:

gui/.streamlit/secrets.toml

With the following content:

server_url = "http://backend:8000/process"

3. Run with Docker Compose

docker-compose up --build

This will build and lift two containers:

Backend at http://localhost:8000
Graphical interface at http://localhost:8501

🌍 Access from another machine (optional)

Make sure you expose the ports correctly ( 8000 , 8501 ).
Use the server machine's IP instead of localhost in secrets.toml .
You can also set up custom Docker networks for cross-host access.

📦 For production

You can run only the backend if you want to integrate it with another interface:

docker build -f Dockerfile.backend -t mcp_backend .
docker run -p 8000:8000 mcp_backend

✨ Example of use

In the web interface, you can type questions like:

¿Qué día es hoy?
¿Dónde estoy?
¿Qué clima hace?
Explícame qué es Python

The app will decide whether to answer the question directly or delegate it to an agent.

🛠️ Agents available

Agent	Function
DATE	Returns the current date and time
LOCATION	Detects the city and country by IP
CLIMATE	Returns the weather at the current location
LLM_EXPERT	Query the `deepseek-r1:7b` model via `ollama`

📄 License

This project is licensed under the MIT License.

🙋‍♂️ Author

This server cannot be installed

security - not tested

license - not found

quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A server that routes user questions to specialized agents (date, location, weather) or an LLM expert, with a simple Streamlit web interface for easy interaction.

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