Leadenrich Mcp

Name: Leadenrich Mcp
Author: carsonlabs

@carsonlabs

代码仓库

关于 Leadenrich Mcp

MCP server for waterfall lead enrichment — cascades Apollo, Clearbit, and Hunter for maximum data coverage

基本信息

分类

其他

运行时

python

传输方式

stdio

发布者

carsonlabs

提交者

carsonroell-debug

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "leadenrich": {
      "command": "npx",
      "args": [
        "leadenrich-mcp"
      ],
      "env": {
        "APOLLO_API_KEY": "<YOUR_APOLLO_KEY>",
        "CLEARBIT_API_KEY": "<YOUR_CLEARBIT_KEY>",
        "HUNTER_API_KEY": "<YOUR_HUNTER_KEY>"
      }
    }
  }
}

工具

Full waterfall enrichment for a single lead (email, domain, or name+domain)

Discover an email from first name + last name + company domain

Company firmographic data by domain (industry, size, revenue, etc.)

Batch enrich up to 25 leads concurrently

Quota, cost tracking, and remaining lookups

Server status, configured providers, and cache stats

概览

What is Leadenrich Mcp?

Leadenrich Mcp is a waterfall lead enrichment server that implements the Model Context Protocol (MCP). It cascades through Apollo, Clearbit, and Hunter to build the most complete lead profile in a single call, returning merged profiles with per-field attribution. It is designed for AI agents like Claude and Cursor to run enrichment workflows directly.

How to use Leadenrich Mcp?

Install via pip install leadenrich-mcp and start the server with leadenrich-mcp (defaults to http://localhost:8300/mcp). Connect from Claude Desktop by adding the server URL to claude_desktop_config.json, or from Claude Code using claude mcp add leadenrich --transport http http://localhost:8300/mcp. Provider API keys (Apollo, Clearbit, Hunter) are set as environment variables; all are optional.

Key features of Leadenrich Mcp

Waterfall enrichment across Apollo, Clearbit, and Hunter
Field attribution showing which provider contributed each data point
Built-in caching to avoid duplicate API calls
Usage metering with free tier and per-lookup pricing
Batch enrichment of up to 25 leads concurrently
Email discovery from first name, last name, and company domain

Use cases of Leadenrich Mcp

Enriching a single lead by email, domain, or name+domain for sales outreach
Discovering a prospect’s email from their name and company domain
Retrieving company firmographic data (industry, size, revenue) for account research
Batch enriching multiple leads to update a CRM or database

FAQ from Leadenrich Mcp

What providers does Leadenrich Mcp use?

Apollo (contact, company, LinkedIn data), Clearbit (person, firmographic data), and Hunter (email finding and verification).

Are all provider API keys required?

No. Each provider key is optional; the server uses whichever providers are configured and skips the rest.

What is the free tier?

The free tier gives 50 lookups per month using only Hunter (single-provider lookups). Pro ($29/mo) unlocks the full three-provider waterfall, email finder, company enrichment, and batch operations.

How does pricing work?

Pricing is per lookup based on how many providers return data: $0.05 for one provider, $0.10 for two, $0.15 for three. Free tier covers 50 lookups/month.

What are the runtime requirements?

Python 3.11+ and pip. The server can run locally, via Docker, or on hosting platforms like Railway and Fly.io.

其他分类下的更多 MCP 服务器

Servers

modelcontextprotocol

Model Context Protocol Servers

88.2K

Activepieces

activepieces

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

23.2K