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Leadpipe MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/7/2026, 7:51:46 PM

Leadpipe MCP

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View RepositoryVisit Website

AI-powered lead qualification engine for MCP. Ingest, enrich, score, and export leads to your CRM.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Remote HTTP
Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "leadpipe-mcp": {
      "url": "https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE"
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Documentation Overview

LeadPipe MCP

AI-powered lead qualification engine for the Model Context Protocol

License: MIT TypeScript MCP

LeadPipe ingests leads from any source, enriches them with company data, scores them 0-100 using configurable AI rules, and exports qualified leads to your CRM β€” all through the MCP protocol.


Features

  • Lead ingestion from webhooks, forms, APIs, or CSV β€” single or batch (up to 100)
  • Auto-enrichment with company data: industry, size, country, tech stack (via Hunter.io or domain heuristics)
  • AI scoring engine (0-100) with 6 weighted dimensions + custom rules
  • CRM export to HubSpot, Pipedrive, CSV, or JSON
  • ICP pre-qualification to filter leads on freemail/title/country/tech-stack before spending a single enrichment credit
  • Pipeline analytics with real-time stats, score distribution, conversion rates
  • Configurable scoring weights, high-value titles/industries, custom rules
  • 10 MCP tools + 3 MCP resources covering the full lead lifecycle

Quick Start

Install from MCPize Marketplace

  1. Search for LeadPipe MCP on mcpize.com
  2. Click Install and select your subscription tier
  3. Tools and resources are automatically available in any MCP-compatible client (Cursor, VS Code, etc.)

Build from Source

bash
git clone https://github.com/enzoemir1/leadpipe-mcp.git
cd leadpipe-mcp
npm ci
npm run build

Add to your MCP client config:

config.json
{
  "mcpServers": {
    "leadpipe": {
      "command": "node",
      "args": ["path/to/leadpipe-mcp/dist/index.js"]
    }
  }
}

Tools

lead_demo_seed

Seed the pipeline with a realistic demo dataset β€” 14 leads across 5 archetypes (hot decision-makers, warm mid-level, cold junior/small-co, raw unenriched, and disqualified) β€” so every downstream tool returns meaningful output without any API keys. Safe to call multiple times; each call appends a fresh batch with new UUIDs.

config.json
{}

Returns: counts by status plus sample_lead_ids you can feed into lead_enrich, lead_score, or lead_export.

lead_qualify

Filter leads against your Ideal Customer Profile before spending any enrichment credits. Uses only locally-available signals β€” email domain, job title, country, industry, company size, tech stack β€” so nothing is charged to Hunter.io, HubSpot, or Pipedrive.

config.json
{
  "criteria": {
    "reject_freemail": true,
    "required_title_keywords": ["vp", "director", "head", "founder"],
    "target_countries": ["US", "CA", "GB"],
    "min_company_size": "11-50",
    "required_tech_stack": ["shopify"]
  },
  "auto_disqualify": true
}

Returns per-lead qualified/rejected decisions with reasons and an estimated credit savings figure.

Pairs well with platform detection tools. If you chain a tool like Detecto (detect_platform) before lead_qualify, the detected tech stack populates company.tech_stack, and required_tech_stack can drop wrong-platform leads before they ever reach enrichment or scoring.

lead_ingest

Add a single lead to the pipeline.

config.json
{
  "email": "jane@acme.com",
  "first_name": "Jane",
  "last_name": "Smith",
  "job_title": "VP of Engineering",
  "company_name": "Acme Corp",
  "company_domain": "acme.com",
  "source": "website_form",
  "tags": ["demo-request"]
}

lead_batch_ingest

Add 1-100 leads at once. Duplicates are automatically skipped.

config.json
{
  "leads": [
    { "email": "lead1@corp.com", "job_title": "CEO" },
    { "email": "lead2@startup.io", "job_title": "CTO" }
  ]
}

lead_enrich

Enrich a lead with company data using the email domain.

config.json
{ "lead_id": "uuid-of-lead" }

Returns: company name, industry, size, country, tech stack, LinkedIn URL.

lead_score

Calculate a qualification score (0-100). Leads scoring 60+ are marked qualified.

config.json
{ "lead_id": "uuid-of-lead" }

Returns score + detailed breakdown across all 6 dimensions.

lead_search

Search and filter leads with pagination.

config.json
{
  "query": "acme",
  "status": "qualified",
  "min_score": 60,
  "limit": 20,
  "offset": 0
}

lead_export

Export leads to CRM or file format.

config.json
{
  "target": "hubspot",
  "min_score": 60
}

Targets: hubspot, pipedrive, csv, json

Google Sheets export is on the roadmap. Currently returns Sheets-ready formatted data.

pipeline_stats

Get pipeline analytics. No input required.

Returns: total leads, status/source breakdown, average score, score distribution, qualified rate, leads today/week/month.

config_scoring

View or update scoring configuration.

config.json
{
  "job_title_weight": 0.30,
  "high_value_titles": ["ceo", "cto", "vp", "founder"],
  "custom_rules": [
    {
      "field": "company_industry",
      "operator": "equals",
      "value": "fintech",
      "points": 15,
      "description": "Bonus for fintech companies"
    }
  ]
}

Resources

ResourceDescription
leads://recentThe 50 most recently added leads
leads://pipelinePipeline summary with status counts, scores, conversion rates
leads://configCurrent scoring engine configuration

Scoring Engine

Leads are scored 0-100 using a weighted average of 6 dimensions:

DimensionDefault WeightHow It Works
Job Title25%C-level/Founder: 100, VP/Director: 85, Manager: 65, Senior: 50, Junior: 15
Company Size20%Preferred sizes (11-50, 51-200, 201-500): 90, others scaled accordingly
Industry20%High-value industries (SaaS, fintech, etc.): 90, others: 40
Engagement15%Phone provided, full name, tags, source type (landing page > CSV)
Recency10%Today: 100, last week: 75, last month: 35, 3+ months: 5
Custom Rules10%User-defined rules with -50 to +50 points each

Formula: score = sum(dimension_score * weight)

Leads with score >= 60 are qualified. Below 60 are disqualified.


CRM Integration

HubSpot

Set the HUBSPOT_API_KEY environment variable with your HubSpot private app access token.

server.ts
export HUBSPOT_API_KEY="pat-xxx-xxx"

Pipedrive

Set the PIPEDRIVE_API_KEY environment variable.

server.ts
export PIPEDRIVE_API_KEY="xxx"

CSV / JSON

No configuration needed. Export returns data directly.


Enrichment

LeadPipe extracts the domain from the lead's email and looks up company data:

  1. Hunter.io (if HUNTER_API_KEY is set) β€” returns organization, industry, country, tech stack
  2. Domain heuristics β€” maps known domains to company data
  3. Freemail detection β€” gmail.com, yahoo.com, etc. are flagged (no company enrichment)

Pricing

TierPriceToolsFeatures
Free€0lead_demo_seed, lead_ingest, lead_batch_ingest, lead_search, lead_score, config_scoringIngest, manual scoring, ICP pre-qualification
Pro€19 lifetime+ lead_qualify, lead_enrich, lead_export, pipeline_statsAI scoring, Hunter.io enrichment, CRM export, pipeline analytics

One-time €19 lifetime license (3 machines) β€” no subscription. See Pro License below to buy and activate.


Development

Terminal
npm run dev        # Hot reload development
npm run build      # Production build
npm test           # Run unit tests
npm run inspect    # Open MCP Inspector

Pro License

LeadPipe ships in Free mode β€” lead_demo_seed, lead_ingest, lead_batch_ingest, lead_search, lead_score, and config_scoring are open. The following tools require a Pro license:

  • lead_qualify β€” ICP pre-filter
  • lead_enrich β€” domain knowledge-base enrichment
  • lead_export β€” HubSpot / Pipedrive / Google Sheets / CSV / JSON
  • pipeline_stats β€” portfolio analytics

Buy a Pro License (€19, lifetime, 3 machines): https://automatiabcn.lemonsqueezy.com/buy/360565a3-2577-45e2-93dd-1548a881f456

Or get the Indie MCP Stack Bundle (€69, all 4 servers).

Then activate by setting the env var:

server.ts
export LEMONSQUEEZY_LICENSE_KEY=YOUR-KEY-HERE

Or in your Claude Desktop / MCP client config:

config.json
{
  "mcpServers": {
    "leadpipe-mcp": {
      "command": "npx",
      "args": ["-y", "leadpipe-mcp-server"],
      "env": { "LEMONSQUEEZY_LICENSE_KEY": "YOUR-KEY-HERE" }
    }
  }
}

Validation is cached locally for 24 h, so the server is fully offline-capable after the first run.


License

MIT License. See LICENSE for details.

Built by Automatia BCN.

Read the full README β†’View source on GitHub β†’

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Reviews

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Frequently Asked Questions about Leadpipe MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "leadpipe-mcp": { "command": "npx", "args": ["-y", "leadpipe-mcp"] } }

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
TransportSSE (Remote)
RuntimeNode.js
Last updatedSep 7, 2026
Views0
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36Quality signal: Fair Β· 36/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools16/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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