MoltSets + LinkedIn Workflows - Zevari Docs

Why pair them

MoltSets is useful as the identity and contact-data layer: verified business emails, verified personal emails, mobile phones, companies, hashes, and LinkedIn URLs. Zevari is the LinkedIn execution layer: live profile and company context, ICP scoring, target persistence, campaign state, inbox triage, and approval-gated LinkedIn writes.

Reference flow

• 1. Resolve

  Call MoltSets to turn an email, company, phone, or search query into a LinkedIn profile or account record.

• 2. Save

  Save the person or company into Zevari with MoltSets source metadata attached, then dedupe against existing targets and campaigns.

• 3. Enrich

  Use Zevari to research live LinkedIn profile, company, post, and signal context before deciding whether the person is worth working.

• 4. Stage

  Stage connection requests, messages, comments, posts, or campaign actions through Zevari's approval gate before any write executes.

Workflow recipes

• Verified email to LinkedIn campaign

  Resolve a verified email to a LinkedIn profile, save it as a target, score fit, and stage a warm-by-default LinkedIn sequence.

• People search to target list

  Use MoltSets to discover contacts by role, seniority, industry, country, company, or free-text query, then use Zevari to qualify and work the LinkedIn side.

• Company visit to account motion

  Map an IP or company search result to an account, find relevant people on LinkedIn, score the account and person fit, then stage a reviewed next action.

Safety model

The key boundary is simple: contact enrichment is not permission to send. Keep MoltSets confidence and source metadata attached to targets, let Zevari enforce sender limits, working hours, pacing, duplicate checks, and approval payloads, and do not send LinkedIn writes directly from enrichment jobs.

Implementation notes

A typical implementation resolves identity through MoltSets, saves a target through Zevari targets.save or the REST target endpoint, scores the target with Zevari ICP scoring, then stages the intended LinkedIn write with confirmations.requestAction. Zevari currently exposes 135 MCP tools and 105 public REST API equivalents in production.

Docs Links

• MoltSets + LinkedIn marketing page
• REST API Reference
• MCP Reference
• Approval gates
• Help Center

  Human-facing guide to Zevari skills, workflows, agents, videos, safety, and support.

• Workflow Guide

  LinkedIn outreach, content, prospect research, audience analysis, campaign, inbox, and GTM workflows for Claude.

• Prompting Guide

  Copy-ready prompts that require AI assistants to read the MCP reference before acting.

• Workspaces and Sender Seats

  Human-facing setup guide for workspaces, members, billing, add-on LinkedIn sender seats, and teammate LinkedIn connections.

• Safety Center

  Human-facing guide to Zevari safety guardrails, warm-up, pauses, blocked actions, and recovery state.

• Warm-Up

  Human-facing guide to LinkedIn sender warm-up and gradual activity ramping.

• API Playbooks

  Ordered REST call flows for developers and AI agents using the Zevari API.

• MoltSets + LinkedIn Workflows

  Use MoltSets verified contact data with Zevari's LinkedIn enrichment, campaign, and approval-gated execution layer.

• MCP Reference

  Agent-facing tool schemas, capability contracts, examples, gotchas, and recovery guidance.

• API Reference

  Public REST API reference powered by the Zevari OpenAPI document.

• MCP Reference JSON

  Machine-readable MCP tool reference.

• LLMs Full

  AI-readable Zevari documentation bundle.

• Support

  Send bug reports, support handoffs, and feature requests.