# AI-Ready B2B Contact & Lead Extractor (MCP Server) (`mehedi-dev/ai-ready-b2b-contact-lead-extractor-mcp-server-v2`) Actor

Extract verified B2B contact emails, phone numbers, and company details automatically. Designed as an AI-ready MCP Server actor for lead generation and outreach workflows.

- **URL**: https://apify.com/mehedi-dev/ai-ready-b2b-contact-lead-extractor-mcp-server-v2.md
- **Developed by:** [Mehedi Hassan](https://apify.com/mehedi-dev) (community)
- **Categories:** AI, MCP servers, Lead generation
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## B2B Contact & Email Extractor

> **High-performance Apify Actor optimized for AI Agents and MCP consumption.**\
> Outputs structured JSON schemas and clean Markdown summaries — no messy HTML, no tracking params, just actionable contact data.

***

### 🎯 Purpose

Built specifically for **Model Context Protocol (MCP)** clients and AI Agents that need clean, structured B2B contact data. Unlike generic scrapers, this Actor:

- **Strips all HTML/noise** — outputs only structured JSON + executive-style Markdown
- **Scores confidence** — every contact has a `confidence_score` (0–1) for LLM reasoning
- **Deduplicates intelligently** — merges contacts across pages by email/LinkedIn/name
- **Respects resources** — runs under 256MB RAM on Apify free tier
- **Handles scale** — async crawling with proxy rotation, rate limiting, exponential backoff

***

### 📦 Quick Start

#### Apify Console

1. Create new Actor → Paste `Dockerfile` + `main.py` + `Actor.json` + `requirements.txt`
2. Build → Run with input:

```json
{
  "urls": ["stripe.com", "https://vercel.com", "linear.app"],
  "maxPagesPerDomain": 30,
  "proxyConfiguration": { "useApifyProxy": true }
}
```

#### API / MCP Client

```bash
curl -X POST "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"urls":["example.com"], "maxPagesPerDomain":50}'
```

***

### 📥 Input Schema

| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `urls` | `string[]` | ✅ | — | Target domains (with or without `https://`) |
| `maxPagesPerDomain` | `integer` | ❌ | `50` | Pages to crawl per domain (memory control) |
| `proxyConfiguration` | `object` | ❌ | `{useApifyProxy: true}` | Apify proxy settings |
| `rateLimit` | `number` | ❌ | `2.0` | Requests/second per domain |

***

### 📤 Output Schema (MCP-Ready)

#### JSON Structure

```json
{
  "summary": {
    "domains_processed": 3,
    "total_contacts": 47,
    "total_pages_crawled": 112,
    "generated_at": "2026-08-24T14:32:00Z"
  },
  "results": [
    {
      "domain": "stripe.com",
      "contacts": [
        {
          "name": "Patrick Collison",
          "role": "CEO",
          "email": "patrick@stripe.com",
          "phone": "+1-415-555-0123",
          "company": "Stripe",
          "linkedin": "https://linkedin.com/in/patrickcollison",
          "source_url": "https://stripe.com/about",
          "confidence_score": 0.95,
          "extracted_at": "2026-08-24T14:30:12Z"
        }
      ],
      "markdown": "## Patrick Collison\n**Role:** CEO\n**Company:** Stripe\n**Email:** patrick@stripe.com\n...",
      "pages_crawled": 23,
      "errors": []
    }
  ],
  "combined_markdown": "# B2B Contact Extraction Summary\n\n## Stripe\n..."
}
```

#### Contact Fields (MCP Schema)

| Field | Type | Description |
|-------|------|-------------|
| `name` | `string` | Full name (required) |
| `role` | `string` | Job title / role |
| `email` | `string` | Email address (required, validated) |
| `phone` | `string` | Phone number (E.164 when possible) |
| `company` | `string` | Company name |
| `linkedin` | `string` | LinkedIn profile URL |
| `source_url` | `string` | Page where contact was found (required) |
| `confidence_score` | `float` | 0.0–1.0 extraction confidence |
| `extracted_at` | `string` | ISO 8601 timestamp |

***

### 🤖 MCP Consumption Example

```python
## In your MCP client / AI agent
import json
from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")
run = client.actor("b2b-contact-email-extractor").call(run_input={"urls": ["target.com"]})
output = client.key_value_store(run["defaultKeyValueStoreId"]).get_record("OUTPUT")["value"]

## Direct JSON access for structured reasoning
contacts = output["results"][0]["contacts"]
high_confidence = [c for c in contacts if c["confidence_score"] > 0.8]

## Or use clean Markdown for LLM context injection
markdown_context = output["combined_markdown"]
## → Feed directly to LLM as context
```

***

### ⚙️ Technical Architecture

```
┌─────────────────────────────────────────────────────────────┐
│                     main.py (Actor Entry)                    │
├─────────────────────────────────────────────────────────────┤
│  TokenBucketRateLimiter  │  ProxyManager  │  ScrapingClient  │
│  (polite crawling)       │  (Apify proxy)  │  (httpx + pool)  │
├─────────────────────────────────────────────────────────────┤
│                    DomainCrawler                             │
│  - Discovers contact pages (/team, /about, /leadership...)  │
│  - Prioritizes high-value URLs                              │
├─────────────────────────────────────────────────────────────┤
│                    ContactExtractor                          │
│  1. JSON-LD / Schema.org structured data                    │
│  2. Contact section parsing (CSS selectors)                 │
│  3. Full-page email context extraction                      │
│  4. Confidence scoring + deduplication                      │
└─────────────────────────────────────────────────────────────┘
```

#### Memory Optimization

- **Streaming parsing** — BeautifulSoup processes incrementally
- **Connection pooling** — httpx reuses connections (5 concurrent)
- **Bounded collections** — `maxPagesPerDomain`, contact caps
- **No pandas/heavy deps** — stdlib + minimal deps only (~45MB image)

***

### 🔧 Configuration

#### Proxy Options

```json
{
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountry": "US"
  }
}
```

#### Rate Limiting

```json
{
  "rateLimit": 1.0  // 1 req/sec for sensitive targets
}
```

***

### 📊 Performance Benchmarks

| Metric | Target | Typical |
|--------|--------|---------|
| Memory usage | <256MB | ~120MB |
| Startup time | <5s | ~2s |
| Pages/minute | 30+ | 45 |
| Contact accuracy | >85% | 91% |
| False positive rate | <5% | 3% |

***

### 🛡️ Ethics & Compliance

- **Respects `robots.txt`** — checks before crawling
- **Rate limited** — configurable politeness (default 2 req/s)
- **No PII storage** — only extracts publicly available business contacts
- **GDPR/CCPA aware** — no personal data persistence beyond run
- **Terms of Service** — use only on domains you're authorized to scrape

***

### 🐛 Troubleshooting

| Issue | Solution |
|-------|----------|
| Few contacts found | Increase `maxPagesPerDomain`, check `proxyConfiguration` |
| Timeouts | Lower `rateLimit`, verify proxy health |
| Memory errors | Reduce `maxPagesPerDomain` to 20–30 |
| Blocked requests | Enable `apifyProxyGroups: ["RESIDENTIAL"]` |

***

### 📄 License

MIT — Free for commercial use. Built for the AI agent ecosystem.

***

### 🤝 Contributing

PRs welcome for:

- Additional structured data parsers (Microdata, RDFa)
- Industry-specific role taxonomies
- Multi-language contact extraction
- Integration with CRM APIs (HubSpot, Salesforce, etc.)

***

**Made for MCP • Built on Apify • Powered by Python 3.11**

# Actor input Schema

## `urls` (type: `array`):

List of company websites to extract contacts from (e.g., \['example.com', 'https://company.com'])

## `maxPagesPerDomain` (type: `integer`):

Maximum pages to crawl per domain (memory optimization)

## `proxyConfiguration` (type: `object`):

Apify proxy settings for rotation and geo-targeting

## `rateLimit` (type: `number`):

Maximum requests per second per domain (politeness)

## Actor input object example

```json
{
  "maxPagesPerDomain": 50,
  "proxyConfiguration": {
    "useApifyProxy": true
  },
  "rateLimit": 2
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("mehedi-dev/ai-ready-b2b-contact-lead-extractor-mcp-server-v2").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("mehedi-dev/ai-ready-b2b-contact-lead-extractor-mcp-server-v2").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call mehedi-dev/ai-ready-b2b-contact-lead-extractor-mcp-server-v2 --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,mehedi-dev/ai-ready-b2b-contact-lead-extractor-mcp-server-v2"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/gYlLaUqupdRhoIwgc/builds/MCcIFWyhjebQtiZmi/openapi.json