Snowflake has released a redesigned Observe MCP server and new CLI that give AI agents direct, cost-efficient access to observability data.
The key change is removing an internal LLM intermediary; agents now connect straight to Observe's APIs, cutting latency and operational costs while opening new use cases like automated alert triage and incident investigation.
Both tools share the same capabilities and come with prebuilt skills for common observability workflows.
What happened
Snowflake announced general availability of a redesigned Observe MCP server and new Observe CLI, both offering full parity—every operation exposed to AI agents via the MCP server is also available as a CLI command. The rollout is happening now across all clusters, with eu-2 and ca-1 to follow shortly.
Why it matters
The new architecture removes an LLM intermediary, letting agents connect directly to Observe's APIs and underlying data structure. This cuts latency, overhead, and operational cost per call, while enabling use cases like automated alert triage and incident investigation copilots that were previously difficult or expensive to build.
What to watch
Both tools ship with prebuilt skills—structured workflows for common observability tasks (incident investigation, tracing failures, detecting outliers)—that work immediately after configuration. Engineers can access the MCP server by logging into their tenant, navigating to "Manage account" → "MCP server details", and following setup instructions for their chosen agent platform.
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Observability platforms have traditionally been built for human users navigating UIs and dashboards. Snowflake's announcement reflects a fundamental shift: as AI agents take on operational responsibilities—triaging alerts, investigating incidents, correlating failures—observability tools must be redesigned to serve both humans and machines. The old Observe MCP server architecture included an internal LLM to interpret agent queries, a design that made sense when agents and foundation models were less capable. With advances in foundation models, that middleman became unnecessary overhead: agents now understand context graphs and can write precise queries themselves.
The removal of the LLM intermediary is not merely a technical optimization; it solves a practical cost and latency problem that engineering teams had already worked around. By giving agents direct access to the same APIs powering the Observe UI, the new architecture enables faster queries, lower operational costs per call, and more accurate results—agents can examine the data structure and choose which datasets to query before formulating a response. The addition of prebuilt skills (structured workflows for incident investigation, failure tracing, and outlier detection) lowers the barrier to entry: teams can start building agent-driven observability workflows immediately without custom setup.
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