MCP Business AI Transformation

You run and consume an enterprise-grade MCP server that orchestrates a multi-agent AI transformation for business domains. It exposes a high-performance API, supports multiple LLM providers, and offers observability, security, and a production-ready deployment path with Docker Compose.

How to use

You interact with the MCP server through its HTTP API or via the local server process you run on your machine. Use it to initialize a session, discover available tools, and execute tools to perform business tasks such as data analysis, API execution, validation, and reporting. The server coordinates specialized agents, routes requests through the MCP protocol hub, and leverages a multi-model AI fabric to deliver results with traceability and observability.

How to install

Follow these steps to set up the MCP server and its environment from scratch. This flow uses Docker Compose for quick onboarding and local development steps for the backend and frontend components.

Prerequisites include Docker and Docker Compose, Node.js 18+ for frontend development, and Python 3.11+ for backend development.

1. Create a configuration file to store API keys and connection details.

# .env
# API Keys
EVOLUTION_API_KEY=your_evolution_api_key
OPENAI_API_KEY=your_openai_api_key
HUGGINGFACE_API_KEY=your_huggingface_api_key

# Security
SECRET_KEY=your-super-secret-key-change-in-production

# Database and caching (optional, defaults work with Docker)
DATABASE_URL=postgresql+asyncpg://postgres:password@localhost:5432/mcp_db
REDIS_URL=redis://localhost:6379

Additional sections

Configuration and security are designed to be straightforward. You authenticate with JWTs for users and API keys for services, and you apply role-based access control to manage permissions. The MCP server is designed to run with Docker Compose, and it exposes a real-time dashboard built with React and TypeScript to monitor tasks, agents, and LLM usage.

Key components include the Agent Layer (orchestrator), the MCP Gateway (protocol hub), and the Business APIs. The system integrates an LLM Fabric, a State Manager (Redis + PostgreSQL), and a Monitoring Hub for observability. Production-grade security and observability are built in via Prometheus, Grafana, and Jaeger.

Configuration and endpoints

You can run the server locally and access the frontend and API endpoints. The following endpoints are commonly used during development and experimentation.

Available tools

Data Analyst

Extracts insights from structured and unstructured data, performs exploratory data analysis, and summarizes findings for business decisions.

API Executor

Executes external API calls as part of task orchestration and integrates results back into task context.

Business Validator

Validates outputs against business rules and data quality criteria to ensure relevance and accuracy.

Report Generator

Produces structured reports and summaries suitable for stakeholders and executives.