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Personal Email to Company Resolver MCP Server

Personal Email to Company Resolver MCP Server

Personal Email to Company Resolver MCP Server

Glama score MCP Registry npm version npm downloads license mcpservers.org

Give it a free mail address (Gmail, Outlook, iCloud, Yahoo, and about 140 more consumer domains). Each run reads the public Gravatar profile for the address, the GitHub account whose public commits are signed with it, and, for a name with an employer, Mamba Labs Person LinkedIn URL Resolver. It returns one row per address with the person's name, likely employer, employer domain, and a confidence. It never contacts the mailbox and never opens LinkedIn.

What's Inside

Related MCP server: xposedornot

What it does

  • match_status is resolved (employer at 0.75 or more, billed), weak, name_only, corporate_email, not_found, invalid_email, or blocked, and only resolved is billed.

  • employer_source says where the employer came from: gravatar, github, web_result, linkedin_headline, or email_domain. evidence_urls lists the public pages.

  • The LinkedIn step raises the confidence when a LinkedIn result shows the employer as current and lowers it when it shows it as former.

All of the work runs on Apify. This package is a thin client that starts the actor run, waits for it, and hands back the dataset rows.

Quick start

You need Node.js 18 or newer and an Apify account with an API token.

Add this to your Claude Desktop config (claude_desktop_config.json), Cursor, or any MCP client:

{
  "mcpServers": {
    "mamba-personal-email-to-company-resolver": {
      "command": "npx",
      "args": [
        "-y",
        "@mambalabsdev/mcp-personal-email-to-company-resolver"
      ],
      "env": {
        "APIFY_TOKEN": "your-apify-token"
      }
    }
  }
}

Restart the client. The resolve_personal_email_employer tool appears.

Prerequisites

Example prompts

  • "These ten signups used Gmail addresses. Which companies do they work at?"

  • "Find the employer behind this Outlook address and show me the evidence."

Tool

resolve_personal_email_employer. One tool. Example arguments:

{ "emails": ["sample.signup@gmail.com"] }

Inputs

Every buyer facing input of the actor, unchanged. Omitted inputs take the actor's own defaults.

Input

Type

Description

email

string

One address

emails

array

Up to 300 addresses

github_token

string

Optional, your own free GitHub token

search_web

string

true adds one exact Google search per address left without an employer. Default false

use_linkedin_resolver

string

true (default) runs Person LinkedIn URL Resolver from your account, billed at its own price

country

string

Google country code. Default us

max_charge_usd

string

Optional cost cap for the run

Pricing

Pay per event on Apify, billed to your Apify account. Prices in USD.

Event

Free plan

Bronze

Silver

Gold

email-resolved, per address with an employer at 0.75 or more

$0.025

$0.02375

$0.0225

$0.02125

apify-actor-start, per run start

$0.0005

$0.0005

$0.0005

$0.0005

Every other row is free. With use_linkedin_resolver on, Person LinkedIn URL Resolver bills its own events on top: $0.035 per person sent to it, found or not, plus $0.0005 per run on the free plan.

Your keys

The actor holds no key. github_token is optional and is your own, used for the run only. Mamba Labs pays the platform costs of the run, proxy traffic included.

Full actor documentation

apify.com/mambalabs/personal-email-to-company-resolver

License

MIT. See LICENSE.

Available Tools

1 tool
resolve_personal_email_employerResolve the employer behind a personal emailA
Read-only

Find the likely employer behind a free mail address (Gmail, Outlook, iCloud, Yahoo, and about 140 more), from the public Gravatar profile for the address, the GitHub account whose public commits are signed with it, and Mamba Labs Person LinkedIn URL Resolver. Returns one row per address with person_name, employer_name, employer_domain when a source names it, employer_source, employer_confidence from 0 to 1, match_status (resolved at 0.75 or more, weak, name_only, corporate_email, not_found, invalid_email, blocked), the LinkedIn URL when found, and evidence_urls. It never contacts the mailbox and never opens LinkedIn. A run that does not succeed is returned as an error with its run ID. Read-only; requires an APIFY_TOKEN; Apify charges per resolved address, and the optional LinkedIn step is billed by Person LinkedIn URL Resolver at its own price.

Inputs: email, emails, country, search_web, +3 more.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.4/5.0

    Scored across 1 tool

    Disambiguation5/5

    There is only one tool, so there is no possibility of misselection between tools. Its purpose (resolve the employer behind a personal email address) is stated explicitly and unambiguously.

    Naming Consistency5/5

    The single name resolve_personal_email_employer is descriptive snake_case with a clear verb_noun_qualifier structure. With only one tool there is no convention clash, and the name reads predictably.

    Tool Count3/5

    A single-purpose resolver plausibly needs only one operation, and the tool accepts multiple addresses per call, so the count is not wrong. However, one tool is a thin surface for a billed, asynchronous Apify actor, leaving no companion operation for run inspection or cost checks.

    Completeness4/5

    The tool covers the full resolution lifecycle for its narrow domain: multiple input addresses, rich per-row output (name, employer, domain, source, confidence, match_status, LinkedIn URL, evidence), and documented failure statuses. The main gap is that failed runs only return a run ID with no tool to retrieve that run's results or status, which is a dead end for recovery.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

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