LeadPipe MCP

AI-powered lead qualification engine for the Model Context Protocol

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 — all through the MCP protocol.

Features

Lead ingestion from webhooks, forms, APIs, or CSV — 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

Search for LeadPipe MCP on mcpize.com
Click Install and select your subscription tier
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 — 14 leads across 5 archetypes (hot decision-makers, warm mid-level, cold junior/small-co, raw unenriched, and disqualified) — 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 — email domain, job title, country, industry, company size, tech stack — 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

Resource	Description
`leads://recent`	The 50 most recently added leads
`leads://pipeline`	Pipeline summary with status counts, scores, conversion rates
`leads://config`	Current scoring engine configuration

Scoring Engine

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

Dimension	Default Weight	How It Works
Job Title	25%	C-level/Founder: 100, VP/Director: 85, Manager: 65, Senior: 50, Junior: 15
Company Size	20%	Preferred sizes (11-50, 51-200, 201-500): 90, others scaled accordingly
Industry	20%	High-value industries (SaaS, fintech, etc.): 90, others: 40
Engagement	15%	Phone provided, full name, tags, source type (landing page > CSV)
Recency	10%	Today: 100, last week: 75, last month: 35, 3+ months: 5
Custom Rules	10%	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:

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

Pricing

Tier	Price	Tools	Features
Free	€0	`lead_demo_seed`, `lead_ingest`, `lead_batch_ingest`, `lead_search`, `lead_score`, `config_scoring`	Ingest, manual scoring, ICP pre-qualification
Pro	€19 lifetime	+ `lead_qualify`, `lead_enrich`, `lead_export`, `pipeline_stats`	AI scoring, Hunter.io enrichment, CRM export, pipeline analytics

One-time €19 lifetime license (3 machines) — 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 — 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 — ICP pre-filter
lead_enrich — domain knowledge-base enrichment
lead_export — HubSpot / Pipedrive / Google Sheets / CSV / JSON
pipeline_stats — 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.