Verified business email
linkedin_to_business_email
MoltSets workflow guide for GTM engineers
MoltSets finds the contact. Zevari turns it into a safe LinkedIn workflow.
MoltSets gives AI agents verified business emails, personal emails, mobile phones, company data, hashes, and LinkedIn identity resolution. Zevari gives those same agents the LinkedIn execution layer: 135 MCP tools, 105 REST API endpoints, live LinkedIn enrichment, campaign state, and approval-gated actions built around known LinkedIn safety limits.
Independent guide. Zevari is not affiliated with MoltSets or LinkedIn.
Agent pipeline
Identity data to LinkedIn action
MoltSets
Resolve
email_to_linkedin, search_people, linkedin_to_mobile_phone
Zevari
Enrich
profile, company, posts, signals, ICP score
Zevari
Stage
connection, message, comment, campaign, inbox
Human
Approve
review payload, approve once, execute inside limits
135
MCP tools
Live tool reference
105
REST endpoints
Public API equivalents
36
Sensitive gates
Writes and risky actions reviewed
Why this page exists
Contact data is not the workflow. It is the input.
A GTM agent can collect verified emails, mobile phones, company records, hashes, and LinkedIn URLs. The hard part comes next: who is worth contacting, what did they just do on LinkedIn, how should the first touch happen, what limit applies to the sender, and which exact write is approved?
Verified personal email
linkedin_to_personal_email
Carrier-verified mobile
linkedin_to_mobile_phone
SHA256 audience keys
linkedin_to_sha256
Company from IP or search
ip_to_company, search_companies
LinkedIn URL from email
email_to_linkedin
Four workflows
How GTM engineers can combine MoltSets and Zevari
Use MoltSets where it is strongest: verified identity and contact data. Use Zevari for the LinkedIn side: live enrichment, campaign construction, approval gates, reply triage, and safety-aware execution.
Email to LinkedIn to live account research
Input
MoltSets resolves a business email or personal email to a LinkedIn profile.
Zevari
Zevari researches the person and company on LinkedIn, scores the fit against your ICP, saves the target, and stages the next action.
Output
A verified identity becomes a reviewed LinkedIn workflow, not another row in a sheet.
People search to campaign list
Input
MoltSets finds GTM targets by role, seniority, industry, country, company, or free-text query.
Zevari
Zevari enriches each LinkedIn profile with live context, dedupes against your workspace, and builds a warm-by-default sequence.
Output
Your agent moves from contact discovery to LinkedIn execution without handing the list to a browser bot.
Verified mobile as a fallback signal
Input
MoltSets returns a carrier-verified mobile phone for a known LinkedIn profile.
Zevari
Zevari still starts on LinkedIn: view, react, comment, connect, then classify replies and only route phone follow-up where your compliance rules allow it.
Output
Phone data supports the GTM motion without turning the first touch into a cold interruption.
Anonymous company visit to account motion
Input
MoltSets maps an IP address to a company or discovers matching accounts from company search.
Zevari
Zevari finds relevant people, recent posts, buying signals, and mutual context on LinkedIn, then stages account-based outreach for approval.
Output
Website intent becomes a specific person, reason, and next step.
Developer shape
Build the enrichment job, then let Zevari hold the LinkedIn state.
Your code can call MoltSets for identity resolution, normalize the target, and call Zevari over REST. Your agent can do the same over MCP. Either way, outbound writes do not fire unattended. Zevari stages sensitive actions, exposes the approval payload, and executes only after approval.
REST
105 public API endpoints
MCP
135 agent-callable tools
Gate
36 sensitive tool contracts
// Example shape: MoltSets identity -> Zevari LinkedIn workflow
const profile = await moltsets.post("/email_to_linkedin", {
email: "buyer@target-account.com"
});
await zevari.post("/v1/targets/save", {
targets: [{
linkedin_url: profile.results.linkedin_url,
work_email: "buyer@target-account.com"
}],
source: "moltsets_email_to_linkedin"
});
await zevari.post("/v1/agents/icpScore", {
identifier: profile.results.linkedin_url,
product_offering: "Managed LinkedIn SDR execution"
});
await zevari.post("/v1/confirmations/requestAction", {
action_type: "linkedin_send_connection_request",
payload: {
linkedin_url: profile.results.linkedin_url,
note: "Grounded connection note goes here"
}
});
// Writes still stop at Zevari's approval gate before execution.Safety layer
The part your data API does not solve
Verified data makes an agent more capable. Zevari keeps that capability inside a controlled LinkedIn operating model.
Every outbound LinkedIn write is staged for human approval before it sends.
Session-based connection, no browser cookies, no password handoff, no extension.
Server-side weekly ceilings, working hours, pacing, duplicate checks, and burst caps.
LinkedIn profile, company, post, campaign, inbox, content, and safety tools share one workspace state.
Public REST API and MCP reference are generated from the same live capability contract.
FAQ
Questions GTM engineers ask
Is Zevari affiliated with MoltSets?
No. Zevari is independent and not affiliated with MoltSets or LinkedIn. This page explains a practical workflow for teams that use MoltSets data and need a safe LinkedIn execution layer.
Why pair MoltSets with Zevari?
MoltSets is useful for identity resolution and verified contact data. Zevari is useful after that: live LinkedIn research, ICP scoring, campaign state, approval-gated connection requests, messages, comments, posts, inbox triage, and safety controls.
Can my agent use Zevari without MCP?
Yes. Zevari exposes 105 public REST API endpoints as well as 135 MCP tools. Use REST from workers, cron jobs, CRMs, enrichment jobs, or custom GTM agents. Use MCP when the operator is Claude, Codex, ChatGPT, or another MCP client.
Does Zevari guarantee my LinkedIn account cannot be banned?
No responsible LinkedIn tool should promise that. Zevari is designed around safety controls: approval gates, pacing, known account ceilings, working hours, duplicate checks, and no browser-cookie automation. The point is to keep agent-driven work inside a controlled operating model.
If MoltSets is the identity layer, make Zevari the LinkedIn layer.
Resolve the person, enrich the context, stage the action, approve the exact payload, and let the campaign continue inside known LinkedIn limits.