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

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

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

Add to your MCP client config:

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

Tools

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.

{
  "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.

{
  "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.

{
  "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.

{ "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.

{ "lead_id": "uuid-of-lead" }

Returns score + detailed breakdown across all 6 dimensions.

lead_search

Search and filter leads with pagination.

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

lead_export

Export leads to CRM or file format.

{
 

... [View full README on GitHub](https://github.com/enzoemir1/leadpipe-mcp#readme)