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AI lead qualification: ICP filter, 0-100 scoring, Hunter.io enrichment, HubSpot/Pipedrive export.
AI lead qualification: ICP filter, 0-100 scoring, Hunter.io enrichment, HubSpot/Pipedrive export.
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry. Trust signals: trusted author (4/4 approved).
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-enzoemir1-leadpipe-mcp": {
"args": [
"-y",
"leadpipe-mcp-server"
],
"command": "npx"
}
}
}From the project's GitHub README.
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.
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"]
}
}
}
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) beforelead_qualify, the detected tech stack populatescompany.tech_stack, andrequired_tech_stackcan drop wrong-platform leads before they ever reach enrichment or scoring.
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"]
}
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" }
]
}
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.
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.
Search and filter leads with pagination.
{
"query": "acme",
"status": "qualified",
"min_score": 60,
"limit": 20,
"offset": 0
}
Export leads to CRM or file format.
{
"target": "hubspot",
"min_score": 60
}
Targets: hubspot, pipedrive, csv, json
Google Sheets export is on the roadmap. Currently returns Sheets-ready formatted data.
Get pipeline analytics. No input required.
Returns: total leads, status/source breakdown, average score, score distribution, qualified rate, leads today/week/month.
View or update scoring configuration.
{
"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"
}
]
}
| 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 |
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.
Set the HUBSPOT_API_KEY environment variable with your HubSpot private app access token.
export HUBSPOT_API_KEY="pat-xxx-xxx"
Set the PIPEDRIVE_API_KEY environment variable.
export PIPEDRIVE_API_KEY="xxx"
No configuration needed. Export returns data directly.
LeadPipe extracts the domain from the lead's email and looks up company data:
HUNTER_API_KEY is set) — returns organization, industry, country, tech stack| Tier | Price | Leads/month | Features |
|---|---|---|---|
| Free | $0 | 25 | Ingest, manual scoring, ICP pre-qualification |
| Pro | $19/mo | 300 | AI scoring, Hunter.io enrichment, CRM export |
| Business | $39/mo | 2,500 | Pipeline analytics, custom rules, priority support |
| Agency | $99/mo | 10,000 | Multi-client, white-label exports |
Available on the MCPize Marketplace.
npm run dev # Hot reload development
npm run build # Production build
npm test # Run unit tests
npm run inspect # Open MCP Inspector
MIT License. See LICENSE for details.
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