Trento MCP Server

Overview

The Trento MCP Server is a Model Context Protocol (MCP) implementation that bridges the gap between AI assistants and the Trento Project. Instead of navigating through web interfaces, you can now manage and monitor your SAP systems by simply conversing with your AI assistant in natural language.

Whether you’re checking cluster health, reviewing system configurations, or analyzing SAP landscapes, the Trento MCP Server enables you to work more efficiently by bringing Trento’s powerful capabilities directly into your AI-powered workflow. It connects to SUSE Linux Enterprise Server (SLES), which provides native agentic AI support through MCPHost integration, and it is also supported by SUSE AI for private, on-premises AI deployments.

What is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard that allows AI applications to securely connect to external data sources and tools. Think of it as a universal adapter that lets your AI assistant - whether it’s GitHub Copilot, Claude Desktop, or any other MCP-compatible tool - interact with enterprise systems like Trento in a consistent, secure way.

See It in Action

Watch this video demonstrating how the Trento MCP Server transforms complex SAP monitoring tasks into simple natural language conversations.

Demo Highlights:

• Query SAP system health status conversationally

• Retrieve cluster configuration details on demand

• Analyze host information across your landscape

Quick Start

Get started with the Trento MCP Server in minutes. For detailed instructions, see the full documentation index.

Prerequisites

Before you begin, ensure you have:

• A running Trento Server (version 3.x or later) - Check out the official documentation for installation guidance.

• An MCP-compatible AI assistant, such as public AI tools like VS Code with GitHub Copilot, Claude Desktop, or Cursor, or private AI platforms like SUSE AI.

• A Trento Personal Access Token - Generated from your Trento user profile for secure API access.

Installation Options

Choose the deployment method that best fits your environment:

Systemd Service (Recommended for Linux servers)

For traditional Linux deployments, such as SUSE Linux Enterprise Server:

# 1. Install the package
sudo zypper install mcp-server-trento

# 2. Configure the service
sudo cp /usr/share/doc/packages/mcp-server-trento/mcp-server-trento.example /etc/trento/mcp-server-trento
sudo vi /etc/trento/mcp-server-trento # Set your TRENTO_URL

# 3. Start the service
sudo systemctl enable --now mcp-server-trento

# The Trento MCP server will be available at
# http://localhost:5000/mcp

Container Deployment (For development/testing)

For Docker or Podman environments:

docker run -d \
  --name mcp-server-trento \
  -p 5000:5000 \
  -e TRENTO_MCP_TRENTO_URL=https://demo.trento-project.io/ \
  -e TRENTO_MCP_TAG_FILTER=MCP \
  registry.opensuse.org/devel/sap/trento/factory/containers/trento/mcp-server-trento:latest

Kubernetes Deployment (For production clusters)

If you’re deploying Trento on Kubernetes using Helm:

helm upgrade --install trento-server oci://registry.suse.com/trento/trento-server \
  --set trento-mcp-server.enabled=true \
  --set trento-mcp-server.mcpServer.trentoURL=https://demo.trento-project.io/
  ... # other flags

The MCP Server will be deployed alongside your Trento installation.

Usage Examples

Once configured, you can interact with Trento through your AI assistant using natural language:

Ask questions conversationally: * "Show me all SAP systems in my landscape" * "What’s the health status of cluster cluster-1?" * "List all hosts running HANA databases" * "Get details about the checks execution history" * "Are there any critical alerts I need to address?"

Get instant insights: * "Summarize the overall health of my SAP environment" * "Which systems need attention today?" * "Show me the latest check results for production systems"

The AI assistant will use the Trento MCP Server to execute these requests and present the results in a conversational format.

Documentation

For comprehensive documentation, see the full documentation index.

Installation and Configuration:

• Installation Guide - Deploy on Kubernetes, systemd, or containers

• Configuration Options - Complete reference of all settings, flags, and environment variables

Integration Guides:

• SLES 16 Integration - Connect using MCPHost on SUSE Linux Enterprise Server 16

• SUSE AI Platform Integration - Integrate with SUSE AI for private, on-premises AI deployments

• Other MCP Hosts - Configure with VS Code, Claude Desktop, Cursor, and more

External Resources:

• Trento Project Website

• Model Context Protocol Documentation

For Developers

The Trento MCP Server is built in Go and acts as a bridge between MCP clients and the Trento API, automatically generating MCP tools from OpenAPI specifications.

Architecture Overview:

┌─────────────────┐         ┌──────────────────┐         ┌─────────────┐
│   AI Assistant  │ ◄─────► │ Trento MCP Server│ ◄─────► │Trento Server│
│  (VS Code, etc) │   MCP   │                  │  HTTP   │   (API)     │
└─────────────────┘         └──────────────────┘         └─────────────┘

Quick Development Setup:

# Clone and run locally
git clone https://github.com/trento-project/mcp-server.git
cd mcp-server
make run

# Build and test
make build
make test
make lint

For comprehensive documentation, see the developer documentation index.

We welcome contributions! Report bugs, request features, or ask questions via GitHub Issues.

License

Copyright 2025 SUSE LLC

This project is licensed under the Apache License 2.0. See the LICENSE file for details.