Salomon Labs
Each one does a single job and hands its result to the next. Published as Apify Actors, callable over API or MCP, priced per run.
5 solutions live
Find YouTube creators by niche, filter them by subscriber count and country, and get their profile, links and public contact email - each one validated with a plain-English send recommendation.
Remove invalid, dead-domain, disposable and role addresses from any email list, and get a 0-100 send-risk score with a plain-English reason for every row.
Give it a company website and get every published email and phone number, each with the page it was found on and a 0-100 send-risk score with a plain-English reason.
Analyze YouTube channels for sponsorship: expected views from recent uploads, audience language inferred from real comments, upload cadence, engagement, whether they already take brand deals, and an estimated price from your own CPM.
Give it a company website and get what that company sells - product categories, brand and product names, whether there is a checkout and on which platform - each fact with the page it came from.
Each tool is independent and none of them calls another. A line above means one produces data another accepts, so the two can be wired together in a workflow or by an agent. These are read from the catalogue rather than written by hand, so the list cannot claim a composition that is not actually possible.
These run as Apify Actors, so an MCP client reaches them through Apify's hosted server at mcp.apify.com. Nothing extra was built to make that work. They are not loaded by default, so an agent either finds them through Apify's search tool or is pointed at them by name:
Which is where composition stops being theoretical. An agent that can call both can find creators and qualify their addresses in one instruction, with nobody assembling a pipeline first.