What changed from the old Observe MCP server?

The original design used a custom LLM harness as an intermediary: agents sent queries to a single endpoint, where Observe's LLM interpreted them and returned answers. The new server removes this LLM middleman, connecting agents directly to Observe's APIs and the context graph, reducing both latency and cost while giving agents direct visibility into data structures.

Can I use the CLI if I'm not running an AI agent?

Yes. The new CLI works in both agent environments (like Claude Code) and traditional interactive terminal sessions. Some workflows run autonomously in the background, while others are designed for interactive use requiring an engineer to guide the investigation.

What can agents do with this access?

Agents can query Observe directly to investigate incidents, trace failures, validate changes, find n+1 issues, and detect outliers using prebuilt skills. Common use cases include automatic alert triage (querying Observe when an alert fires for debugging context) and copilots that assist humans during incident investigations.

Large Language ModelsAI Coding AssistantsSnowflake AI BlogAug 14, 2026Aug 14, 2026, 13:01 JST

Observe launches agent-ready CLI and redesigned MCP server for AI access to telemetry

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3 Key Points

What happened
Snowflake's Observe platform announced general availability of a redesigned MCP (model context protocol) server and a new CLI tool that give AI agents direct programmatic access to observability data. The new architecture removes an embedded LLM intermediary, connecting agents directly to Observe's APIs instead; full parity means every MCP operation is also available as a CLI command.
Why it matters
As AI agents increasingly analyze production telemetry—from debugging errors to correlating service failures—observability platforms must serve both human operators and machines. The direct API access reduces latency and cost compared to the old LLM-mediated design, and enables engineers to build custom alert-triage agents and copilots that automate incident investigation workflows without waiting for human intervention.
What to watch
Both tools ship with prebuilt skills for common tasks (incident investigation, failure tracing, n+1 detection, outlier detection) and work immediately after configuration. Access the MCP server through your Observe tenant's "Manage account" → "MCP server details" page; rollout is live across all clusters, with eu-2 and ca-1 following shortly.

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Context & Analysis

The announcement reflects a fundamental shift in how observability platforms must operate: as AI agents become routine readers and investigators of telemetry data, access patterns change from interactive UI-driven exploration to programmatic, autonomous queries. Snowflake recognized this by soliciting feedback from engineering teams already building custom agents on top of Observe, learning that teams had resorted to workarounds due to lack of supported programmatic access.

The architectural rethink also reveals a broader industry trend: as foundation models mature, embedding a separate LLM layer within a tool becomes wasteful. Removing Observe's LLM intermediary and letting agents call APIs directly is not just faster—it passes cost savings to customers who no longer pay for redundant inference. The new design gives agents the same data-access capabilities available to humans through the UI (APM service maps, alert listings, OpenTelemetry setup), but without latency or expense overhead. Engineers can now compose and automate observability workflows with prebuilt skills, enabling alert triage, debugging, and incident response to happen with minimal human intervention.

FAQ

What changed from the old Observe MCP server?: The original design used a custom LLM harness as an intermediary: agents sent queries to a single endpoint, where Observe's LLM interpreted them and returned answers. The new server removes this LLM middleman, connecting agents directly to Observe's APIs and the context graph, reducing both latency and cost while giving agents direct visibility into data structures.
Can I use the CLI if I'm not running an AI agent?: Yes. The new CLI works in both agent environments (like Claude Code) and traditional interactive terminal sessions. Some workflows run autonomously in the background, while others are designed for interactive use requiring an engineer to guide the investigation.
What can agents do with this access?: Agents can query Observe directly to investigate incidents, trace failures, validate changes, find n+1 issues, and detect outliers using prebuilt skills. Common use cases include automatic alert triage (querying Observe when an alert fires for debugging context) and copilots that assist humans during incident investigations.

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