Python in Count just got a major upgrade:
✅ Install almost any package
✅ Work with as much data as the job needs — not just what a browser tab can hold
✅ Let the Count agent write, run, and review the Python for you
✅ Schedule it, refresh it, and fire alerts
✅
Managing agents and context across a whole org is hard. This week we shipped three ways to make Count's agent your own ↓
→ Context for Connections — teach the agent about a specific data source. No semantic layer required.
→ Context via API — manage context from dbt, git,
Count now connects to any #MCP server as a data source — same as plugging in a database.
That means your support calls, docs, and the context scattered across your stack are suddenly things you can actually analyse. Slack, HubSpot, Stripe, Linear, Notion, GitHub, Intercom,
"Jim is unsure if our sales are all over the place, or if we're killing it on Forex arbitrage.”
Count lets you build catalogs of trustable, governed, and highly usable metrics. Making your numbers always mean the same thing, in the same format, and same currency.
“What was that number you told me last week? The one about session sources”
I tell you lots of numbers. Every week.
Breathe.
Built on our Count Metrics semantic layer, "Explore" lets everyone in the business quickly work with governed metrics and dimensions themselves.
