Description
We use several systems to manage DNS at SUSE and openSUSE: BIND, external providers, PowerDNS... each of them is managed in a different way either with raw zones (BIND) or Terraform (external providers).
DNSControl is an opinionated tool to manage DNS as code while being provider agnostic. It's developed and used by StackExchange and was spearheaded by Tom Limoncelly.
Implementing DNSControl should allow us to have a single DNS operations interface that end users can leverage.
This would reduce complexity for end users as they can use a single simplified ECMAScript based DSL instead of BIND zones for internal and HCL config for external.
Operations for our IT orgnaization would be greatly reduced. DNSControl itself has several internal checks that reduce our need to do linting and we can concentrate on implementing logical checks based on ownership.
This simplifies reviews a lot and the integration with BIND and providers allows our IT organization to implement an apply on merge.
At an organizational level it will separate our DNS tasks from other IT operations, speeding up DNS changes and allowing us to delegate DNS reviews to service desk or even customer teams through CODEOWNERS.
Goals
- Create a test subdomain in one of our internal BIND servers to be managed with DNSControl.
- Create an internal DNSControl repository to implement gitops for DNS.
- Deploy DNS changes strictly through gitops.
Extended goals
- Implement CODEOWNERS.
- Replicate main goals for external DNS.
Resources
Looking for hackers with the skills:
This project is part of:
Hack Week 25
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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 Python web server (e.g., using FastAPI) 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