Operations | Monitoring | ITSM | DevOps | Cloud

Chaos Engineering and reliability testing give you visibility into the actual reliability of your services by simulating real-world failure conditions. But what if you could dig into the testing and results data using AI to quickly uncover new insights? That’s the logic behind the Gremlin MCP Server. Released as part of Reliability Intelligence, the Gremlin MCP Server allows you to bring your LLM of choice to explore your Gremlin data and find opportunities to get more out of Gremlin.

Note: A version of this post originally appeared on the OpenTelemetry blog. Victoria Nduka is user experience designer and open source contributor making her way into the cloud native space. She writes about design, accessibility, and open source with the same curiosity she brings to her work. On May 29, I wrapped up my mentorship with Prometheus through the Linux Foundation mentorship program.

A few months ago, I built an MCP server for Toronto’s Open Data portal so an agent could fetch datasets relevant to a user’s question. I threw the first version together, skimmed the code, and everything looked fine. Then I asked Claude: “What are all the traffic-related data sources for the city of Toronto?” The tool call fired. I got relevant results. And then I hit an error: “Conversation is too long, please start a new conversation.” I had only asked one question.