Enrichment workflows in Snov.io MCP

This guide shows four data enrichment workflows you can run from your AI tool with Snov.io MCP.

Fill in emails and phone numbers, add LinkedIn data, research prospects and companies, and add results directly to your lists.

Each workflow runs several enrichment actions in sequence. Describe the goal in plain words, and the assistant runs the actions after you confirm the cost.

Choose a workflow
Workflow 1. Fill in emails and phone numbers
Workflow 2. Add LinkedIn data to a list
Workflow 3. Research companies and find a reason to reach out
Workflow 4. Clean list data before outreach
Save prompts and manage columns
Next steps

Before you start. To run these workflows, make sure you have:

1. Snov.io connected to your AI tool. Follow this guide to connect it.

2. A prospect list that you own. Only the list owner can run enrichment.

3. Enrichment tokens. Read this guide to learn how tokens work.

4. Starting data for each prospect. Each workflow lists what it needs.

Every workflow follows the same run pattern: check the cost, test on three to five prospects, then run the whole list. Read this guide for those steps and for checking progress.

Choose a workflow

Workflow 1. Fill in emails and phone numbers

Add contact details to a list, and spend phone tokens only on the prospects you plan to call.

Email search needs a LinkedIn URL, or a first name, last name, and company domain or name. Phone search also works with a first name, last name, and email.

Find emails for prospects without one

Ask for a search of only the prospects that have no email yet. The assistant shows the cost and the provider options before it runs.

Find phone numbers for priority prospects

Phone searches cost more than email searches, so run them on a short list. Name the accounts you plan to call.

Data you get

Found emails and numbers appear in the Email and Phone number columns of your list. To check which prospects returned a result, run the prompt below. It lists each prospect as found, not found, or in progress, with the saved emails and numbers.

You are charged only when an email or a phone number is found.

Workflow 2. Add LinkedIn data to a list

Pull profile details and extra LinkedIn data into your list, then move the values you need into their own columns.

A LinkedIn URL works best. The Snov.io database also accepts a first name, last name, and company domain or email. Generect needs a LinkedIn URL or an email.

Pull profile details

Choose the Snov.io database option, which updates monthly, or the real-time option from Generect, which pulls live LinkedIn data.

Pull extra LinkedIn data

Each run adds one data type: publications, patents, awards, recommendations, profile activity, or similar profiles.

Run this step again with a different data type to add more context to the same list.

Extract the values you need into separate columns

Profile and company enrichments store many values in one bulk column.

This step extracts data from that column into a single column, such as job title, company name, or start date from the work history. Extracting values spends no tokens. Read this guide to learn more about prospect data in Snov.io.

Data you get

Each value sits in its own column. Use those columns as variables in campaign emails and LinkedIn messages, as described in this guide.

Workflow 3. Research companies and find a reason to reach out

Give each prospect one short, company-specific line for outreach.

Each prospect needs a company domain, company name, or company LinkedIn URL. Prospects with none of these are skipped.

Get company details

This adds industry, size, location, specialties, funding, and similar companies to each prospect.

Research each company on the web

Ask for one specific fact and a column name. The assistant shows the cost for the default model and for cheaper models before it runs. See this guide for more research prompts.

Web research charges tokens for every prospect, even when no public data is found.

Turn the research into one outreach line

This step uses Clean and edit to read the research column and write the line you can use in a message. Prospects with No data found have no news to mention, so skip them in outreach.

Ask which enrichment columns and prompt variables the list has before you write a prompt that uses them.

Data you get

The Outreach reason column holds one sentence per prospect. Use it as a variable in your first email line.

Workflow 4. Clean list data before outreach

Standardize company names, job titles, and locations with one prompt per field.

You need at least one existing column with the data you want to change. Each prompt saves its result to a column.

Clean company names

Reformat locations

Review the cleaned values

Ask for a few prospects before and after cleanup.

Save prompts and manage columns

Save a prompt that works, so you can run it again on any list. Preset prompts for research, data cleanup, and personalization appear next to your own.

Preset prompts can only be marked as favorites. You can edit or delete your own prompts. Deleting a column removes it with all its values, so the assistant asks you to confirm first.

Next steps

• Add the enriched list to a Campaign, and insert the new columns as variables in your email steps
• Use the same columns in messages sent with LinkedIn Automation
• Read this guide to compare every enrichment action and its cost in the app