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MCP server · RAG

io.github.carsonroell-debug/leadenrich-mcp

Waterfall lead enrichment — cascades Apollo, Clearbit, and Hunter for max coverage

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Scored 6 months ago · breakdown

About io.github.carsonroell-debug/leadenrich-mcp

io.github.carsonroell-debug/leadenrich-mcp is an MCP server in the RAG category: waterfall lead enrichment — cascades Apollo, Clearbit, and Hunter for max coverage.

Install

uvx
uvx leadenrich-mcp
pip
pip install leadenrich-mcp

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Tools (6)

check_usageQuota, cost tracking, and remaining lookups

Parameters not published.

enrich_batchBatch enrich up to 25 leads concurrently

Parameters not published.

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

Parameters not published.

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

Parameters not published.

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

Parameters not published.

health_checkServer status, configured providers, and cache stats

Parameters not published.

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README

LeadEnrich MCP Server

PyPI MCP MCPize

Waterfall lead enrichment for AI agents. Cascades through Apollo, Clearbit, and Hunter to build the most complete lead profile in a single call.

One-click install: Install on MCPize | pip install leadenrich-mcp

LeadEnrich MCP exposes lead and company enrichment through the Model Context Protocol (MCP), so tools like Claude and Cursor can run enrichment workflows directly. Give it an email, domain, or name and it returns a merged profile with field attribution showing which provider contributed each data point.

Quick Connect

1. Install and start the server

pip install leadenrich-mcp
leadenrich-mcp

The server starts on http://localhost:8300/mcp by default.

2a. Connect from Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "leadenrich": {
      "url": "http://localhost:8300/mcp"
    }
  }
}

2b. Connect from Claude Code

claude mcp add leadenrich --transport http http://localhost:8300/mcp

Tools

Tool Description
enrich_lead Full waterfall enrichment for a single lead (email, domain, or name+domain)
find_email Discover an email from first name + last name + company domain
enrich_company Company firmographic data by domain (industry, size, revenue, etc.)
enrich_batch Batch enrich up to 25 leads concurrently
check_usage Quota, cost tracking, and remaining lookups
health_check Server status, configured providers, and cache stats

How It Works

LeadEnrich uses a waterfall strategy: each provider fills gaps left by the previous one. When email is known, all providers run concurrently for speed. When only name+domain is provided, Apollo discovers the email first, then Clearbit and Hunter run in parallel.

Input (email / domain / name+domain)
         |
         v
  +-----------+     +-----------+     +----------+
  |   Apollo  | --> |  Clearbit | --> |  Hunter  |
  +-----------+     +-----------+     +----------+
  |  Contact  |     |  Company  |     |  Email   |
  |  Company  |     |  Person   |     |  Verify  |
  |  LinkedIn |     |  Firmo    |     |  Domain  |
  +-----------+     +-----------+     +----------+
         |               |                |
         v               v                v
  +------------------------------------------+
  |        Merged Profile                    |
  |  16+ fields with per-field attribution   |
  |  Confidence score + lookup cost          |
  +------------------------------------------+

Each field in the result includes attribution so you know exactly which provider it came from. No duplicate API calls thanks to built-in caching.

Pricing

Tier Cost Details
Free $0.00 50 lookups/month
1 provider hit $0.05/lookup Single provider returned data
2 providers hit $0.10/lookup Two providers contributed fields
3 providers hit $0.15/lookup Full waterfall, maximum coverage

Requirements

  • Python 3.11+
  • pip

Quick Start

git clone https://github.com/carsonlabs/leadenrich-mcp.git
cd leadenrich-mcp
pip install -r requirements.txt
python main.py

MCP endpoint:

  • http://localhost:8300/mcp

Environment Variables

Variable Description Required
APOLLO_API_KEY Apollo.io API key No
CLEARBIT_API_KEY Clearbit API key No
HUNTER_API_KEY Hunter.io API key No
LEADENRICH_API_KEY Client auth key for usage metering No
LEADENRICH_FREE_TIER_LIMIT Free tier limit (default: 50) No
PORT Server port (default: 8300) No

All provider keys are optional. The server uses whichever providers are configured and skips the rest.

Example:

export APOLLO_API_KEY="your-apollo-key"
export CLEARBIT_API_KEY="your-clearbit-key"
export HUNTER_API_KEY="your-hunter-key"
python main.py

Running Options

Run directly:

python main.py

Run via FastMCP CLI:

fastmcp run main.py --transport streamable-http --port 8300

Tool Details

enrich_lead

Inputs:

  • email (optional): Contact email address (best identifier)
  • domain (optional): Company domain (e.g. "stripe.com")
  • first_name / last_name (optional): Contact name (combine with domain)
  • providers (optional): Limit which providers to use
  • api_key (optional): Your LeadEnrich API key

Returns merged lead profile with field attribution, confidence score, and lookup cost.

find_email

Inputs:

  • first_name (required): Contact's first name
  • last_name (required): Contact's last name
  • domain (required): Company domain

Returns discovered email with confidence score and verification status.

enrich_company

Input:

  • domain (required): Company domain

Returns company-level firmographic data: industry, size, revenue, description, location.

enrich_batch

Inputs:

  • leads (required): List of lead objects (max 25), each with optional email/domain/name
  • providers (optional): Limit which providers to use
  • api_key (optional): Your LeadEnrich API key

Returns list of enriched profiles with batch summary.

check_usage

Input:

  • api_key (optional): Your LeadEnrich API key

Returns usage stats: lookup count, cost, tier, remaining quota, and cache stats.

health_check

No input. Returns server status, configured providers, cache stats, and version info.

Try It

fastmcp list-tools main.py
fastmcp call-tool main.py health_check '{}'
fastmcp call-tool main.py enrich_lead '{"email":"jane@stripe.com"}'
fastmcp call-tool main.py find_email '{"first_name":"Jane","last_name":"Smith","domain":"stripe.com"}'
fastmcp call-tool main.py enrich_company '{"domain":"stripe.com"}'

Deployment

Smithery

This repo includes smithery.yaml for Smithery deployment.

  1. Push repository to GitHub
  2. Create/add server in Smithery
  3. Point Smithery to this repository

Docker / Hosting Platforms

A Dockerfile is included for Railway, Fly.io, and other container hosts.

# Railway
railway up

# Fly.io
fly launch
fly deploy

Set your provider API keys in your host environment.

Architecture

Agent (Claude, Cursor, etc.)
  -> MCP
LeadEnrich MCP Server (this repo)
  -> Apollo API    (contact + company data)
  -> Clearbit API  (person + firmographic data)
  -> Hunter API    (email finding + verification)

This server is a translation layer between MCP tool calls and multiple enrichment provider APIs, with built-in caching, usage metering, and waterfall merge logic.

Free vs Pro

Tool Free Pro ($29/mo)
enrich_lead Yes (Hunter only) Yes (full waterfall: Apollo + Clearbit + Hunter)
check_usage Yes Yes
health_check Yes Yes
find_email - Yes
enrich_company - Yes
enrich_batch - Yes

Free tier gives you single-provider lookups via Hunter. Pro unlocks the full 3-provider waterfall, email finder, company enrichment, and batch operations.

Upgrade to Pro on MCPize — $29/mo or $290/yr.


Built by Freedom Engineers

Related

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About io.github.carsonroell-debug/leadenrich-mcp

How do I install io.github.carsonroell-debug/leadenrich-mcp?

Run uvx leadenrich-mcp, then add the server to your MCP client's configuration.

Is io.github.carsonroell-debug/leadenrich-mcp safe to use with an AI agent?

Its trust score is 37 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is io.github.carsonroell-debug/leadenrich-mcp still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.