Veeam Backup and Replication MCP Server for Amazon S3

The VBR MCP Server bridges the gap between large language models and enterprise backup infrastructure. It exposes a Model Context Protocol interface that lets AI assistants query Veeam Backup & Replication (VBR) and Amazon S3, retrieving repository metadata, storage usage, and cost information. By standardizing this interaction, developers can build intelligent agents that automate backup monitoring, troubleshoot failures, and optimize storage spend without writing custom integration code.

This server solves the pain point of manual data extraction from VBR and S3. Traditionally, administrators would log into the VBR console or use PowerShell scripts to pull repository lists and cost reports. The MCP server consolidates these operations into a single, well‑defined API surface. An AI assistant can issue natural language requests such as “show s3 cost for repository XYZ for the month of March,” and the server translates them into VBR API calls, queries S3 billing tags, and returns a concise answer. This eliminates repetitive scripting, reduces human error, and enables continuous monitoring within AI‑driven workflows.

Key capabilities include:

• Repository discovery – list all VBR repositories, filter by S3 usage, and retrieve detailed attributes such as size, status, and backup schedule.
• Cost aggregation – read cost tags (e.g., ) from S3 buckets to compute monthly or weekly expenses, supporting both standard and Glacier storage classes.
• Debug logging – all interactions are recorded in a directory, facilitating troubleshooting and audit trails.
• Secure AWS integration – the server relies on standard AWS credentials (access key, secret, session token) to authenticate against Bedrock and S3 services.

Real‑world scenarios where this MCP server shines include:

• Operational dashboards that feed AI assistants with up‑to‑date backup health metrics, allowing rapid incident response.
• Cost optimization tools that prompt users to migrate infrequently accessed data from S3 Standard to Glacier based on monthly spend reports.
• Compliance monitoring where an agent verifies that all repositories have the required cost tags and alerts on missing or mis‑tagged buckets.

Integration into AI workflows is straightforward: an LLM can call the MCP server’s endpoints as part of a larger prompt, combine the returned data with other context (e.g., ticketing information), and generate actionable recommendations or automated remediation steps. The server’s design follows MCP best practices, making it a drop‑in component for any AI‑centric infrastructure automation stack.