TexAu wraps Apify scrapers behind one API and one MCP server. Claude can call Apify directly. For GTM engineers and technical RevOps who want one programmable surface for every scraper.
Built for GTM engineers and technical RevOps.
What you can build
Three workflows worth shipping with Apify.
Wrap any Apify actor as an MCP tool
Pick an actor, expose it through TexAu, call it from Claude or your API with one config line.
Schedule scrapers from a Table
Driven by row state: a Table row triggers the actor, the result writes back to the same row.
Compose scrapers into a waterfall
Chain multiple Apify actors with TexAu enrichment in between, single trace, automatic retries.
The catalog
Every Apify action TexAu supports.
Grouped by verb. Each row links to the full input/output schema and a try-it widget.
Enrich · 2
Other · 7
Search LinkedIn Sales Navigator People (Apify)ApifyScrape DataGet LinkedIn Posts from Profile (Apify)ApifyScrape DataSearch LinkedIn Companies (Apify)ApifyScrape DataSearch LinkedIn Sales Navigator Accounts (Apify)ApifyScrape DataCrawl Website Content (Apify)ApifyScrape DataScrape Website to Markdown (Apify)ApifyScrape DataGet LinkedIn Job Listings (Apify)ApifyScrape DataThe trace
How a Apify workflow runs.
Apify → TexAu enrichment → Apify writeback. One trace, every step observable.
ApifyApifyAuth & setup
Connect Apify in four steps.
- 1
Generate an API key in Apify
Apify settings → API → Create new key.
- 2
Open Studio → Connections → Apify
Paste the key. We store it encrypted at rest.
- 3
We test the connection on save
Bad key? You see the error inline before any workflow runs.
- 4
Run your first action
Pick any action from the catalog above and try it live.
MCP for Apify
Bring Apify into Claude, Cursor, or any MCP client.
Drop this into your config. Every action above becomes a tool the model can call.
{ "mcpServers": { "texau-apify": { "command": "npx", "args": ["-y", "@texau/mcp-server"], "env": { "TEXAU_API_KEY": "...", "TEXAU_PLATFORM": "apify" }, "tools": [ "apify-linkedin-sales-navigator-people-search", "apify-linkedin-profile-search", "apify-linkedin-post-search-get-items", "apify-linkedin-company-search-get-items", "apify-linkedin-sales-navigator-account-search" ] } } }
Compared
Where TexAu wins vs native Apify automation.
Enrichment depth
Native: one provider. TexAu: 32-integration waterfall with attribution.
Branching logic
Native: linear. TexAu: real conditionals, retries, async-safe steps.
AI surface
Native: none. TexAu: every action is also an MCP tool.
Migration
From direct Apify scripts to TexAu.
Most Apify teams land on TexAu after they hit the ceiling of direct Apify scripts. The pattern is the same: a workflow worked for the first 10 contacts, then it broke at 1,000. Steps timed out. Branching turned into a Frankenstein. Enrichment was always one provider deep, so half the rows came back empty.
TexAu replaces the brittle middle. The 32-integration waterfall fills the rows that direct Apify scripts left empty. Async-safe steps mean a slow LinkedIn fetch does not block the whole run. Branching is real branching, not nested filters. And every action you build for Apify is also an MCP tool. Claude can call it, your API can call it, your Studio operator can run it from a Table.
We import existing workflows. We do not ask you to rebuild from scratch. Most teams are running the same automations on TexAu within a week, with deeper enrichment and observable traces from day one.
Connect Apify in Studio →
Free to start. No credit card. Every action above runs in your first session.