Description
Provide an MCP Server implementation for customers to access data on scc.suse.com via MCP protocol. The core benefit of this MCP interface is that it has direct (read) access to customer data in SCC, so the AI agent gets enhanced knowledge about individual customer data, like subscriptions, orders and registered systems.
Architecture
Goals
We want to demonstrate a proof of concept to connect to the SCC MCP server with any AI agent, for example gemini-cli or codex. Enabling the user to ask questions regarding their SCC inventory.
For this Hackweek, we target that users get proper responses to these example questions:
- Which of my currently active systems are running products that are out of support?
- Do I have ready to use registration codes for SLES?
- What are the latest 5 released patches for SLES 15 SP6? Output as a list with release date, patch name, affected package names and fixed CVEs.
- Which versions of kernel-default are available on SLES 15 SP6?
Technical Notes
Similar to the organization APIs, this can expose to customers data about their subscriptions, orders, systems and products. Authentication should be done by organization credentials, similar to what needs to be provided to RMT/MLM. Customers can connect to the SCC MCP server from their own MCP-compatible client and Large Language Model (LLM), so no third party is involved.
Milestones
[x] Basic MCP API setup MCP endpoints [x] Products / Repositories [x] Subscriptions / Orders [x] Systems [x] Packages [x] Document usage with Gemini CLI, Codex
Resources
Gemini CLI setup:
~/.gemini/settings.json:
{
"mcpServers": {
"SCC local http": {
"httpUrl": "http://localhost:3000/mcp",
"note": "MCP server for SUSE Customer Center"
}
}
}
Results
After 5 days of hackweek, we have good results for the 4 example questions from above:
This project is part of:
Hack Week 25
Activity
Comments
Similar Projects
Bugzilla goes AI - Phase 1 by nwalter
Description
This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.
Goals
To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.
Project Charter
Description
Project Achievements during Hackweek
In this file you can read about what we achieved during Hackweek.
Multi-agent AI assistant for Linux troubleshooting by doreilly
Description
Explore multi-agent architecture as a way to avoid MCP context rot.
Having one agent with many tools bloats the context with low-level details about tool descriptions, parameter schemas etc which hurts LLM performance. Instead have many specialised agents, each with just the tools it needs for its role. A top level supervisor agent takes the user prompt and delegates to appropriate sub-agents.
Goals
Create an AI assistant with some sub-agents that are specialists at troubleshooting Linux subsystems, e.g. systemd, selinux, firewalld etc. The agents can get information from the system by implementing their own tools with simple function calls, or use tools from MCP servers, e.g. a systemd-agent can use tools from systemd-mcp.
Example prompts/responses:
user$ the system seems slow
assistant$ process foo with pid 12345 is using 1000% cpu ...
user$ I can't connect to the apache webserver
assistant$ the firewall is blocking http ... you can open the port with firewall-cmd --add-port ...
Resources
Language Python. The Python ADK is more mature than Golang.
https://google.github.io/adk-docs/
https://github.com/djoreilly/linux-helper
The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio
Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it. 
The Plan
Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!
Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:
❥ The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.
❥ The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.
❥ Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.
If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.
Why?
We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.
The CONCLUSION!!!
A 
State of the Union document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below! 
Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo
Description
Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.
Goals
Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:
- Gain insight into the latest AI trends, tools, and architectural concepts.
- Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).
Resources
Red Hat AI Topic Articles
- https://www.redhat.com/en/topics/ai
Kubeflow Documentation
- https://www.kubeflow.org/docs/
Q4 2025 CNCF Technology Landscape Radar report:
- https://www.cncf.io/announcements/2025/11/11/cncf-and-slashdata-report-finds-leading-ai-tools-gaining-adoption-in-cloud-native-ecosystems/
- https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf
Agent-to-Agent (A2A) Protocol
- https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
Enable more features in mcp-server-uyuni by j_renner
Description
I would like to contribute to mcp-server-uyuni, the MCP server for Uyuni / Multi-Linux Manager) exposing additional features as tools. There is lots of relevant features to be found throughout the API, for example:
- System operations and infos
- System groups
- Maintenance windows
- Ansible
- Reporting
- ...
At the end of the week I managed to enable basic system group operations:
- List all system groups visible to the user
- Create new system groups
- List systems assigned to a group
- Add and remove systems from groups
Goals
- Set up test environment locally with the MCP server and client + a recent MLM server [DONE]
- Identify features and use cases offering a benefit with limited effort required for enablement [DONE]
- Create a PR to the repo [DONE]
Resources
Intelligent Vulnerability Detection for Private Registries by ibone.gonzalez
Description:
This project wants to build an MCP server that connects your LLM to your private registry. It fetches vulnerability reports, probably generated by Trivy, with all the CVEs, and uses the LLM to develop the exact terminal commands or containers updates needed to resolve them.
Goals:
Our goal is to build an MCP for private registries that:
Detects Vulnerabilities: Proactively finds risks in your packages.
Automates Security: Keeps software secure with automated checks and updates.
Fits Your Workflow: Integrates seamlessly so you never leave your tools.
Protects Privacy: Delivers actionable insights without compromising private data.
To provide automated, privacy-first security for private packages that deliver actionable risk alerts directly within the developer’s existing workflow.
Resources:
Code:
Enable more features in mcp-server-uyuni by j_renner
Description
I would like to contribute to mcp-server-uyuni, the MCP server for Uyuni / Multi-Linux Manager) exposing additional features as tools. There is lots of relevant features to be found throughout the API, for example:
- System operations and infos
- System groups
- Maintenance windows
- Ansible
- Reporting
- ...
At the end of the week I managed to enable basic system group operations:
- List all system groups visible to the user
- Create new system groups
- List systems assigned to a group
- Add and remove systems from groups
Goals
- Set up test environment locally with the MCP server and client + a recent MLM server [DONE]
- Identify features and use cases offering a benefit with limited effort required for enablement [DONE]
- Create a PR to the repo [DONE]
Resources
SUSE Edge Image Builder MCP by eminguez
Description
Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.
Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.
Goals
- Familiarize myself with MCPs
- Unrealistic: Have an MCP that can generate an EIB config file
Resources
Result
https://github.com/e-minguez/eib-mcp
I've extensively used antigravity and its agent mode to code this. This heavily uses https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema for the MCP to be built.
I've ended up learning a lot of things about "prompting", json schemas in general, some golang, MCPs and AI in general :)
Example:
Generate an Edge Image Builder configuration for an ISO image based on slmicro-6.2.iso, targeting x86_64 architecture. The output name should be 'my-edge-image' and it should install to /dev/sda. It should deploy
a 3 nodes kubernetes cluster with nodes names "node1", "node2" and "node3" as:
* hostname: node1, IP: 1.1.1.1, role: initializer
* hostname: node2, IP: 1.1.1.2, role: agent
* hostname: node3, IP: 1.1.1.3, role: agent
The kubernetes version should be k3s 1.33.4-k3s1 and it should deploy a cert-manager helm chart (the latest one available according to https://cert-manager.io/docs/installation/helm/). It should create a user
called "suse" with password "suse" and set ntp to "foo.ntp.org". The VIP address for the API should be 1.2.3.4
Generates:
``` apiVersion: "1.0" image: arch: x86_64 baseImage: slmicro-6.2.iso imageType: iso outputImageName: my-edge-image kubernetes: helm: charts: - name: cert-manager repositoryName: jetstack
SUSE Observability MCP server by drutigliano
Description
The idea is to implement the SUSE Observability Model Context Protocol (MCP) Server as a specialized, middle-tier API designed to translate the complex, high-cardinality observability data from StackState (topology, metrics, and events) into highly structured, contextually rich, and LLM-ready snippets.
This MCP Server abstract the StackState APIs. Its primary function is to serve as a Tool/Function Calling target for AI agents. When an AI receives an alert or a user query (e.g., "What caused the outage?"), the AI calls an MCP Server endpoint. The server then fetches the relevant operational facts, summarizes them, normalizes technical identifiers (like URNs and raw metric names) into natural language concepts, and returns a concise JSON or YAML payload. This payload is then injected directly into the LLM's prompt, ensuring the final diagnosis or action is grounded in real-time, accurate SUSE Observability data, effectively minimizing hallucinations.
Goals
- Grounding AI Responses: Ensure that all AI diagnoses, root cause analyses, and action recommendations are strictly based on verifiable, real-time data retrieved from the SUSE Observability StackState platform.
- Simplifying Data Access: Abstract the complexity of StackState's native APIs (e.g., Time Travel, 4T Data Model) into simple, semantic functions that can be easily invoked by LLM tool-calling mechanisms.
- Data Normalization: Convert complex, technical identifiers (like component URNs, raw metric names, and proprietary health states) into standardized, natural language terms that an LLM can easily reason over.
- Enabling Automated Remediation: Define clear, action-oriented MCP endpoints (e.g., execute_runbook) that allow the AI agent to initiate automated operational workflows (e.g., restarts, scaling) after a diagnosis, closing the loop on observability.
Hackweek STEP
- Create a functional MCP endpoint exposing one (or more) tool(s) to answer queries like "What is the health of service X?") by fetching, normalizing, and returning live StackState data in an LLM-ready format.
Scope
- Implement read-only MCP server that can:
- Connect to a live SUSE Observability instance and authenticate (with API token)
- Use tools to fetch data for a specific component URN (e.g., current health state, metrics, possibly topology neighbors, ...).
- Normalize response fields (e.g., URN to "Service Name," health state DEVIATING to "Unhealthy", raw metrics).
- Return the data as a structured JSON payload compliant with the MCP specification.
Deliverables
- MCP Server v0.1 A running Golang MCP server with at least one tool.
- A README.md and a test script (e.g., curl commands or a simple notebook) showing how an AI agent would call the endpoint and the resulting JSON payload.
Outcome A functional and testable API endpoint that proves the core concept: translating complex StackState data into a simple, LLM-ready format. This provides the foundation for developing AI-driven diagnostics and automated remediation.
Resources
- https://www.honeycomb.io/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine
- https://www.datadoghq.com/blog/datadog-remote-mcp-server
- https://modelcontextprotocol.io/specification/2025-06-18/index
- https://modelcontextprotocol.io/docs/develop/build-server
Basic implementation
- https://github.com/drutigliano19/suse-observability-mcp-server
Results
Successfully developed and delivered a fully functional SUSE Observability MCP Server that bridges language models with SUSE Observability's operational data. This project demonstrates how AI agents can perform intelligent troubleshooting and root cause analysis using structured access to real-time infrastructure data.
Example execution
Bugzilla goes AI - Phase 1 by nwalter
Description
This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.
Goals
To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.
Project Charter
Description
Project Achievements during Hackweek
In this file you can read about what we achieved during Hackweek.
Review SCC team internal development processes by calmeidadeoliveira
Description
Continue with the Hackweek 2024, with focus on reviewing existing processes / ways of working and creating workflows:
Goals
- Check all the processes from [1] and [3]
- move them to confluence [4], make comments, corrections, etc.
- present the result to the SCC team and ask for reviews
Resources
[1] https://github.com/SUSE/scc-docs/blob/master/team/workflow/kanban-process.md [2] https://github.com/SUSE/scc-docs/tree/master/team [3] https://github.com/SUSE/scc-docs/tree/master/team/workflow
Confluence
[4] https://confluence.suse.com/spaces/scc/pages/1537703975/Processes+and+ways+of+working