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.
This project is part of:
Hack Week 25
Activity
Comments
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about 1 month ago by lkocman | Reply
This might be interesting together with https://hackweek.opensuse.org/25/projects/opensuse-community-edition-of-packtrack
So we would have a new issue assignment experience for openSUSE contributors.
AI handling assignments of new issues (rather than bnc-triage team from past) and maintainers using (newly as an option) packtrac to actually see what issues are assigned to them.
Both would modernize the issue triage and issue progressing experience.
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17 days ago by rtsvetkov | Reply
Hi, I work on a similar smaller project. Perhaps it will be interesting to incorporate it in your approach.
It doesn't give direct recommendation, but evaluates which aspect of the bug is critical for a decision - recommends the question critical for the decision. GenAI-Powered Systemic Bug Evaluation and Management Assistant
Reply to rtsvetkov
Hi, I work on a similar smaller project. Perhaps it will be interesting to incorporate it in your approach.
It doesn't give direct recommendation, but evaluates which aspect of the bug is critical for a decision - recommends the question critical for the decision. GenAI-Powered Systemic Bug Evaluation and Management Assistant
# A First Level Header ## A Second Level Header Use one asterisk to *emphasize* Use two asterisks for **strong emphasis** - Use hyphens - for unordereed - lists This is an [link to example.com](http://example.com/) This is an image  This is a user link @hans This is a project link hw#some-cool-title
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Description
Two years ago, I evaluated solar routers as part of hackweek24, I've assembled one and it is running almost smoothly.
However, its code quality is not perfect and the codebase doesn't have any testcase (which is tricky, since it is embedded code and rely on getting external data to react).
Before improving the code itself, a testsuite should be created to ensure code additional don't cause regression.
Goals
Create a testsuite, allowing to test solar router code in a virtual environment. Using LLM to help to create this test suite.
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Resources
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.
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Scope
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Resources
- https://www.honeycomb.io/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine
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- https://modelcontextprotocol.io/docs/develop/build-server
Basic implementation
- https://github.com/drutigliano19/suse-observability-mcp-server
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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
Song Search with CLAP by gcolangiuli
Description
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You can have a look at the result here:
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References
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- Free Music Archive: Creative Commons songs that can be used for testing;
Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0
Self-Scaling LLM Infrastructure Powered by Rancher
Description
The Problem
Running LLMs can get expensive and complex pretty quickly.
Today there are typically two choices:
- Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
- Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.
What if there was a middle ground?
What if infrastructure scaled itself instead of making you scale it?
Can we use existing Rancher capabilities like CAPI, autoscaling, and GitOps to make this simpler instead of building everything from scratch?
Project Repository: github.com/alexander-demicev/llmserverless
What This Project Does
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A complete, self-scaling LLM infrastructure that:
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How It Works
A combination of open source tools working together:
Flow:
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Extended private brain - RAG my own scripts and data into offline LLM AI by tjyrinki_suse
Description
For purely studying purposes, I'd like to find out if I could teach an LLM some of my own accumulated knowledge, to use it as a sort of extended brain.
I might use qwen3-coder or something similar as a starting point.
Everything would be done 100% offline without network available to the container, since I prefer to see when network is needed, and make it so it's never needed (other than initial downloads).
Goals
- Learn something about RAG, LLM, AI.
- Find out if everything works offline as intended.
- As an end result have a new way to access my own existing know-how, but so that I can query the wisdom in them.
- Be flexible to pivot in any direction, as long as there are new things learned.
Resources
To be found on the fly.
Timeline
Day 1 (of 4)
- Tried out a RAG demo, expanded on feeding it my own data
- Experimented with qwen3-coder to add a persistent chat functionality, and keeping vectors in a pickle file
- Optimizations to keep everything within context window
- Learn and add a bit of PyTest
Day 2
- More experimenting and more data
- Study ChromaDB
- Add a Web UI that works from another computer even though the container sees network is down
Day 3
- The above RAG is working well enough for demonstration purposes.
- Pivot to trying out OpenCode, configuring local Ollama qwen3-coder there, to analyze the RAG demo.
- Figured out how to configure Ollama template to be usable under OpenCode. OpenCode locally is super slow to just running qwen3-coder alone.
Day 4 (final day)
- Battle with OpenCode that was both slow and kept on piling up broken things.
- Call it success as after all the agentic AI was working locally.
- Clean up the mess left behind a bit.
Blog Post
Summarized the findings at blog post.
Uyuni Health-check Grafana AI Troubleshooter by ygutierrez
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This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.
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Song Search with CLAP by gcolangiuli
Description
Contrastive Language-Audio Pretraining (CLAP) is an open-source library that enables the training of a neural network on both Audio and Text descriptions, making it possible to search for Audio using a Text input. Several pre-trained models for song search are already available on huggingface
Goals
Evaluate how CLAP can be used for song searching and determine which types of queries yield the best results by developing a Minimum Viable Product (MVP) in Python. Based on the results of this MVP, future steps could include:
- Music Tagging;
- Free text search;
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The code for this project will be entirely written using AI to better explore and demonstrate AI capabilities.
Result
In this MVP we implemented:
- Async Song Analysis with Clap model
- Free Text Search of the songs
- Similar song search based on vector representation
- Containerised version with web interface
We also documented what went well and what can be improved in the use of AI.
You can have a look at the result here:
Future implementation can be related to performance improvement and stability of the analysis.
References
- CLAP: The main model being researched;
- huggingface: Pre-trained models for CLAP;
- Free Music Archive: Creative Commons songs that can be used for testing;
MCP Perl SDK by kraih
Description
We've been using the MCP Perl SDK to connect openQA with AI. And while the basics are working pretty well, the SDK is not fully spec compliant yet. So let's change that!
Goals
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- Tool/Prompt/Resource update notifications
- Dynamic Tool/Prompt/Resource lists
- New authentication mechanisms
Resources
Enable more features in mcp-server-uyuni by j_renner
Description
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- 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
MCP Trace Suite by r1chard-lyu
Description
This project plans to create an MCP Trace Suite, a system that consolidates commonly used Linux debugging tools such as bpftrace, perf, and ftrace.
The suite is implemented as an MCP Server. This architecture allows an AI agent to leverage the server to diagnose Linux issues and perform targeted system debugging by remotely executing and retrieving tracing data from these powerful tools.
- Repo: https://github.com/r1chard-lyu/systracesuite
- Demo: Slides
Goals
Build an MCP Server that can integrate various Linux debugging and tracing tools, including bpftrace, perf, ftrace, strace, and others, with support for future expansion of additional tools.
Perform testing by intentionally creating bugs or issues that impact system performance, allowing an AI agent to analyze the root cause and identify the underlying problem.
Resources
- Gemini CLI: https://geminicli.com/
- eBPF: https://ebpf.io/
- bpftrace: https://github.com/bpftrace/bpftrace/
- perf: https://perfwiki.github.io/main/
- ftrace: https://github.com/r1chard-lyu/tracium/
issuefs: FUSE filesystem representing issues (e.g. JIRA) for the use with AI agents code-assistants by llansky3
Description
Creating a FUSE filesystem (issuefs) that mounts issues from various ticketing systems (Github, Jira, Bugzilla, Redmine) as files to your local file system.
And why this is good idea?
- User can use favorite command line tools to view and search the tickets from various sources
- User can use AI agents capabilities from your favorite IDE or cli to ask question about the issues, project or functionality while providing relevant tickets as context without extra work.
- User can use it during development of the new features when you let the AI agent to jump start the solution. The issuefs will give the AI agent the context (AI agents just read few more files) about the bug or requested features. No need for copying and pasting issues to user prompt or by using extra MCP tools to access the issues. These you can still do but this approach is on purpose different.
Goals
- Add Github issue support
- Proof the concept/approach by apply the approach on itself using Github issues for tracking and development of new features
- Add support for Bugzilla and Redmine using this approach in the process of doing it. Record a video of it.
- Clean-up and test the implementation and create some documentation
- Create a blog post about this approach
Resources
There is a prototype implementation here. This currently sort of works with JIRA only.
GenAI-Powered Systemic Bug Evaluation and Management Assistant by rtsvetkov
Motivation
What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?
Let's make GenAI look on the bug from the systemic point and evaluate what we don't know. Which piece of information is missing to take a decision?
Description
To build a tool that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style or Systemic questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.
Goals
Set up a Python environment
Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).
Build the Dialogue Loop
- Write a basic Python script using the Gemini API.
- Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation
Socratic/Systemic Strategy Implementation
- Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
- Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
- Implement Bugzillla call to collect the
- Implement Questioning Framework as LLVM pre-conditioning
- Define set of instructions
- Assemble the Tool
Resources
What are Systemic Questions?
Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.
Gitlab Project
gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question
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
Docs Navigator MCP: SUSE Edition by mackenzie.techdocs
Description
Docs Navigator MCP: SUSE Edition is an AI-powered documentation navigator that makes finding information across SUSE, Rancher, K3s, and RKE2 documentation effortless. Built as a Model Context Protocol (MCP) server, it enables semantic search, intelligent Q&A, and documentation summarization using 100% open-source AI models (no API keys required!). The project also allows you to bring your own keys from Anthropic and Open AI for parallel processing.
Goals
- [ X ] Build functional MCP server with documentation tools
- [ X ] Implement semantic search with vector embeddings
- [ X ] Create user-friendly web interface
- [ X ] Optimize indexing performance (parallel processing)
- [ X ] Add SUSE branding and polish UX
- [ X ] Stretch Goal: Add more documentation sources
- [ X ] Stretch Goal: Implement document change detection for auto-updates
Coming Soon!
- Community Feedback: Test with real users and gather improvement suggestions
Resources
- Repository: Docs Navigator MCP: SUSE Edition GitHub
- UI Demo: Live UI Demo of Docs Navigator MCP: SUSE Edition
Try AI training with ROCm and LoRA by bmwiedemann
Description
I want to setup a Radeon RX 9600 XT 16 GB at home with ROCm on Slowroll.
Goals
I want to test how fast AI inference can get with the GPU and if I can use LoRA to re-train an existing free model for some task.
Resources
- https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html
- https://build.opensuse.org/project/show/science:GPU:ROCm
- https://src.opensuse.org/ROCm/
- https://www.suse.com/c/lora-fine-tuning-llms-for-text-classification/
Results
got inference working with llama.cpp:
export LLAMACPP_ROCM_ARCH=gfx1200
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$LLAMACPP_ROCM_ARCH \
-DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
-Dhipblas_DIR=/usr/lib64/cmake/hipblaslt/ \
&& cmake --build build --config Release -j8
m=models/gpt-oss-20b-mxfp4.gguf
cd $P/llama.cpp && build/bin/llama-server --model $m --threads 8 --port 8005 --host 0.0.0.0 --device ROCm0 --n-gpu-layers 999
Without the --device option it faulted. Maybe because my APU also appears there?
I updated/fixed various related packages: https://src.opensuse.org/ROCm/rocm-examples/pulls/1 https://src.opensuse.org/ROCm/hipblaslt/pulls/1 SR 1320959
benchmark
I benchmarked inference with llama.cpp + gpt-oss-20b-mxfp4.gguf and ROCm offloading to a Radeon RX 9060 XT 16GB. I varied the number of layers that went to the GPU:
- 0 layers 14.49 tokens/s (8 CPU cores)
- 9 layers 17.79 tokens/s 34% VRAM
- 15 layers 22.39 tokens/s 51% VRAM
- 20 layers 27.49 tokens/s 64% VRAM
- 24 layers 41.18 tokens/s 74% VRAM
- 25+ layers 86.63 tokens/s 75% VRAM (only 200% CPU load)
So there is a significant performance-boost if the whole model fits into the GPU's VRAM.
Extended private brain - RAG my own scripts and data into offline LLM AI by tjyrinki_suse
Description
For purely studying purposes, I'd like to find out if I could teach an LLM some of my own accumulated knowledge, to use it as a sort of extended brain.
I might use qwen3-coder or something similar as a starting point.
Everything would be done 100% offline without network available to the container, since I prefer to see when network is needed, and make it so it's never needed (other than initial downloads).
Goals
- Learn something about RAG, LLM, AI.
- Find out if everything works offline as intended.
- As an end result have a new way to access my own existing know-how, but so that I can query the wisdom in them.
- Be flexible to pivot in any direction, as long as there are new things learned.
Resources
To be found on the fly.
Timeline
Day 1 (of 4)
- Tried out a RAG demo, expanded on feeding it my own data
- Experimented with qwen3-coder to add a persistent chat functionality, and keeping vectors in a pickle file
- Optimizations to keep everything within context window
- Learn and add a bit of PyTest
Day 2
- More experimenting and more data
- Study ChromaDB
- Add a Web UI that works from another computer even though the container sees network is down
Day 3
- The above RAG is working well enough for demonstration purposes.
- Pivot to trying out OpenCode, configuring local Ollama qwen3-coder there, to analyze the RAG demo.
- Figured out how to configure Ollama template to be usable under OpenCode. OpenCode locally is super slow to just running qwen3-coder alone.
Day 4 (final day)
- Battle with OpenCode that was both slow and kept on piling up broken things.
- Call it success as after all the agentic AI was working locally.
- Clean up the mess left behind a bit.
Blog Post
Summarized the findings at blog post.
Uyuni Health-check Grafana AI Troubleshooter by ygutierrez
Description
This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.
Goals
- Investigate if and how the
grafana-llm-appplug-in can be used within the Uyuni Health-check tool. - Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
- Evaluate support for local LLMs and external APIs through the plugin.
- Evaluate if and how the Uyuni MCP server could be integrated as another source of information.
Resources
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/
Explore LLM evaluation metrics by thbertoldi
Description
Learn the best practices for evaluating LLM performance with an open-source framework such as DeepEval.
Goals
Curate the knowledge learned during practice and present it to colleagues.
-> Maybe publish a blog post on SUSE's blog?
Resources
https://deepeval.com
https://docs.pactflow.io/docs/bi-directional-contract-testing
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
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
MCP Server for SCC by digitaltomm
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:
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
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
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: