Sentry's MCP is about to get a whole lot more useful with traces.
came across this awesome code snippet on new sentry MCP server updates
before/after visualization of trace spans in Sentry's tree view,
Github repo in comments below
showing how the new MCP server enriches observability output:
Before: spans show only raw names (POST, invoke_agent, SELECT, workflow, job failed) — you can't tell what kind of operation each one is, what model was called, or what failed.
After: each span is decorated with structured OpenTelemetry attributes — op type (http.server, gen_ai.invoke_agent, db.query, rpc.client, exception), the actual target (anthropic/claude-opus-4-6, api.anthropic.com/v1/messages, search_events, issues table), status (200, OK, ValueError), and duration.
Why it matters for MCP/agent debugging:
you can now see the full agent loop at a glance — a 123s invoke_agent call to Claude Opus 4.6, with a 21s LLM round-trip and a 4s tool execution (search_events) nested inside, plus the downstream Postgres + gRPC calls and the eventual ValueError that killed the job.
The shift: span names stay human-readable, but the semantic meaning (gen_ai.*, db.*, http.* conventions) lives in attributes — so Sentry's MCP server can query/group/filter traces by operation type instead of regex-matching span strings.
Basically, it's Sentry adopting OpenTelemetry GenAI semantic conventions so AI agent traces become actually debuggable.