Point your AI at a lead — get back a fit score and a ready-to-send email.
Below isn't a screenshot. A live agent calls our tools and reasons over real data — it scores the person and drafts the outreach, then profiles the account behind them. Same data layer, two doors: MCP for your AI, REST for your code. On the right, the artifact it builds as it goes.
Score the lead, draft the outreach
analyze_contact — a fit score, a written email + LinkedIn, and the do/don't. email_intel checks the address first.
watch it check the ICP, verify the address, then score + draft on the left
Profile the account behind them
company_signals + company_relationships — is now the time, with what, and through whom.
watch it think and reach for Aidenix on the left
Every endpoint, one token
How to connect — this is a real MCP server
{
"mcpServers": {
"aidenix": {
"url": "https://mcp.aidenix.com/mcp",
"headers": { "Authorization": "Bearer ‹token›" }
}
}
}What the agent sees
Every tool returns two things: a human-readable takeaway (the agent reads it and reasons on it) and the full structured block (for its workflow). No target-company employee names in the response — only business signals.
10.8B people & company docs · scoring and drafts weighted by your saved ICP · LinkedIn snapshot 2026-06, sources coresignal + contora + discolike + progai.