Omlet MCP

A while ago, we explored different LLM use cases through “Skillet”. During our experiments, we quickly realized the advantages of using tools, precise integration with observability data sources, and sparse tokenization. The ecosystem has been evolving rapidly, and we've seen MCP emerge as a protocol for flexible tool use. Fortunately, we were able to apply our existing knowledge to quickly move into the Agentic AI Era.

Exploring Maximas in the AI Space

Sometimes, it's beneficial to wait and see how different parts of the ecosystem, especially in AI, develop. The image shows a pattern in various stages of the current AI phase and partly in past AI phases. At different times, the industry has claimed that true automation is finally here and that AI will solve all the problems that trouble enterprises. However, this has often fallen short due to:

1. Integration challenges

2. Inconsistent usage

3. Inaccuracy and scalability issues from a hardware perspective

I believe we are seeing some signs of a "global" peak in the usefulness of AI for data analysis.

Less is more for MCP tools

We quickly realized that humans are significantly affected by context switching and having "too many tools." From our past experience with LLMs and observability data, we also knew that using different payloads could confuse the LLM. With this in mind, we focused on the essentials for our MCP server:

1. Read data from OTel formatted tables

2. Read data and cluster

This "confinement" allows a reasoning model with tool-use abilities to focus on the basics without getting distracted.

What’s next?

As we mentioned, things are moving quickly, but we can use this growing knowledge to adapt fast. The guiding principle should always be: Are we helping engineering, architecture, and ops teams solve issues quickly and consistently? Are we assisting teams in resolving issues when they're unavailable or need breaks? Are we doing this throughout the organization?