BrainBlog for Mezmo by Jason Bloomberg
Observability depends upon telemetry – the data streaming from various applications, services, and systems that indicate their internal state in real-time. Various tools consume such telemetry to enable both operational and cybersecurity tasks.
Telemetry pipelines like Mezmo’s have long supported the full range of Ops and SecOps tooling by processing, enriching, and normalizing all the various telemetry streams that feed such tools.
Today, virtually every Ops and SecOps tool leverages AI to provide insights into observability data while supporting operators with human language interfaces that improve productivity and efficiency.
This exploding demand for data to drive AI models requires an integration protocol that supports the context that drives such insights.
To this end, the model context protocol (MCP) has entered the technology lexicon, providing an AI-friendly protocol that acts as a bridge between AI agents and other applications and various external data sources.
Included in the MCP standard is the concept of an MCP server, which acts as a ‘smart adapter’ that supports AI requests. Doesn’t it make sense, therefore, for a telemetry pipeline like Mezmo’s to support as an MCP server?
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Image courtesy of Mezmo.
