Observe adds MCP server and CLI for AI agents to query operational data

Observability platforms have traditionally been built for human operators—dashboards, UI visualizations, and manual query interfaces. As AI agents become integrated into incident response and debugging workflows, the data models and access patterns that work for humans no longer suffice. Coding assistants that investigate errors before an engineer opens a ticket, or AI SREs that correlate failures across services, require programmatic APIs and structured data access that the UI-first approach cannot provide efficiently.

Snowflake's redesign reflects direct feedback from engineering teams already deploying agents against Observe. Several teams had built their own workarounds because the previous MCP server lacked supported programmatic access. The original architecture's key bottleneck was economic and technical: a custom LLM intermediary that interpreted natural-language queries meant agents could not explore datasets directly, incurred latency on every call, and forced Observe to absorb the cost of running an LLM harness. By removing the LLM layer entirely and exposing direct API access, the new MCP server and CLI lower operational cost, reduce latency, and grant agents the fine-grained visibility they need to write efficient queries. This shift reflects the broader industry recognition that agents and humans will read the same observability data through different interfaces—CLI, MCP, and UI—and that platforms must optimize for both.