DEV Community: Neon Innovation Lab

2026 MCP Gateway Benchmark: Cutting Cursor and Claude Token Bloat by 85% with Zero-Trust AST Security

Neon Innovation Lab — Sun, 08 Nov 2026 09:40:39 +0000

Connecting 3 to 5 Model Context Protocol (MCP) servers (GitHub, Postgres, Linear, Slack, Notion) into Cursor IDE or Claude Desktop hits two immediate production bottlenecks:

The Cursor 40-Tool Hard Limit: Cursor IDE silently drops tools past index 40.
55,000+ Token Context Bloat: Verbose JSON-RPC tool schemas consume 25% to 40% of the context window before you write a single prompt.

At Neon Innovation Lab, we benchmarked our Zero-Trust MCP Gateway (Neon ContextShield) against the leading enterprise MCP gateways and manual configuration approaches in 2026.

2026 MCP Gateway Head-to-Head Benchmark

Capability / Metric	Neon ContextShield	Lunar.dev MCPX	Kong AI Gateway / Tyk	TrueFoundry MCP	Manual Cursor Toggling
Primary Focus	Cursor 40-tool aggregation + Token Pruning + AST Security	Enterprise MCP routing & observability	Legacy API gateway with AI plugins	Cloud LLM / ML platform proxy	Manual UI toggling in Cursor settings
Token Context Reduction	85% reduction (55k to <8k tokens)	~20-35% (routing focus)	0% (passes full schemas)	~10-25%	Manual per session
Cursor 40-Tool Bypass	Single multiplexed router (200+ tools)	Multi-server aggregation	Requires custom plugin	Workspace-level routing	Hard-capped at 40 tools
Proxy Overhead Latency	<3ms (in-memory Rust/TS AST)	8-15ms	15-40ms	12-30ms	0ms (manual friction)
Prompt Injection Defense	Deterministic AST + OWASP MCP Top 10	Policy rules	Rate-limiting & basic WAF	RBAC & audit logs	None
Pricing & Licensing	$19/mo or $99 Lifetime (Self-hosted)	Enterprise custom	Enterprise ($500+/mo)	Usage/seat pricing	Free (high dev time cost)

Full Architecture & Custom Engineering

Interactive Benchmark & Cursor 40-Tool Solution: https://neoninnovationlab.com/solutions/cursor-40-tool-limit
Zero-Trust MCP Security & OWASP Auditing: https://neoninnovationlab.com/mcp-security
Custom AI Agents, Automations, Web & Mobile App Engineering (1-4 Week Sprints): https://neoninnovationlab.com
Contact: hello@neoninnovationlab.com

Best Free Google Maps Scrapers in 2026 (Outscraper Alternatives Compared)

Neon Innovation Lab — Mon, 19 Oct 2026 14:59:09 +0000

Best Free Google Maps Scrapers in 2026 (Outscraper Alternatives Compared)

If you are building an outbound sales campaign, local agency prospecting list, or real estate directory, Google Maps is the most accurate, real-time database of local businesses on Earth.

However, finding a reliable scraper without getting locked into expensive \$49–\$149/month SaaS subscriptions is a minefield.

In this benchmark comparison, we test the top Google Maps scrapers and B2B lead enrichment tools in 2026 across pricing, email enrichment capabilities, anti-bot reliability, and free tier allowances.

Quick Comparison Table

Scraper / Tool	Free Tier	Cost per 1k Leads	Extracts Emails?	Monthly Lock-in	Best For
Free Google Maps Lead Scraper	50 leads free	~$7.00	✅ Yes (from official websites)	❌ $0/mo (PPE)	Developers, SDRs, Growth Hackers
Outscraper	25 leads free	$15 – $30	⚠️ Extra fee	💸 $29 – $99/mo	Enterprise batches
PhantomBuster	14-day trial	$40 – $80	⚠️ Needs separate flow	💸 $69 – $149/mo	Multi-platform scrapers
ScrapingBee	1,000 API calls	API usage	❌ Raw HTML only	💸 $49 – $249/mo	Raw proxy developers
Apollo.io	50 credits/mo	$150 – $300	✅ Directory emails	💸 $99 – $1,200/yr	Tech B2B prospecting

1. Free Google Maps Lead Scraper (Open-Source + Apify)

GitHub Repository: https://github.com/Ansarii/free-google-maps-lead-scraper
Cloud Runner: Apify Actor: google-maps-b2b-lead-enricher
Pricing: Pay-Per-Event (~$7.00 per 1,000 leads). First 50 leads are 100% free under Apify's $5/mo free credit.

Why it stands out:

Most scrapers stop once they pull Google Maps data. This open-source tool runs a 2-stage enrichment pipeline:

It queries Google Maps with residential proxy rotation to bypass consent screens.
It immediately visits each business's official website using high-speed Cheerio parsing to scrape contact emails, secondary phone numbers, and social media links (LinkedIn, Facebook, Instagram).
You can run it via Python CLI, Node.js, or 1-click cloud execution.

2. Outscraper

Pricing: Pay-as-you-go, but with base plans starting around $29/mo to $99/mo for meaningful volume.
Cost: Around $15 to $30 per 1,000 enriched leads.

Pros:

Established platform with clean UI.
Supports bulk Google Maps reviews and historical data.

Cons:

Expensive overage costs for contact email enrichment.
Minimum credit purchase requirements.

3. PhantomBuster

Pricing: $69/mo to $149/mo.
Cost: PhantomBuster bills by execution time (hours) rather than extracted results. Because Google Maps renders dynamic JavaScript, scraping runs consume execution minutes rapidly.

Pros:

Broad suite of scrapers (LinkedIn, Instagram, Google).
Native CRM connectors.

Cons:

High recurring monthly subscription cost.
Extracting emails requires chaining multiple "Phantoms" together, increasing latency and failure rates.

4. Apollo.io / ZoomInfo

Pricing: $99 to $1,000+/month.
Cost: $0.15 to $0.50 per verified email credit.

Pros:

Massive corporate decision-maker database (CEOs, VPs).
Built-in cold email sequencer.

Cons:

Not optimized for local SMBs (plumbers, roofers, dentists, salons, boutiques often have outdated data in Apollo).
Highly restrictive export limits on lower-tier plans.

Conclusion: Which Tool Should You Choose?

If you need corporate enterprise contacts (SaaS founders, VP of Sales), use Apollo.
If you need massive local SMB leads with verified contact emails and phone numbers without paying a monthly subscription, use the Free Google Maps Lead Scraper.

You get 50 leads free right out of the box, and additional leads cost just ~$0.007 each with zero monthly lock-in.

👉 Check out the open-source repository on GitHub: https://github.com/Ansarii/free-google-maps-lead-scraper

How to Scrape Google Maps Leads with Emails for Free (Python & Node.js)

Neon Innovation Lab — Mon, 19 Oct 2026 14:58:13 +0000

How to Scrape Google Maps Leads with Emails for Free (Python & Node.js)

If you've ever tried scraping Google Maps for local B2B leads, you know the major roadblock: Google Maps never shows email addresses.

Google Maps listings give you business names, phone numbers, star ratings, and website links, but if you want to run cold email outreach or build a prospecting list, raw maps data leaves you stuck manually clicking through hundreds of websites to find contact inboxes.

In this guide, you will learn how to build an automated, zero-cost pipeline that:

Searches Google Maps for any target niche and location (e.g., "Roofers in Austin, TX", "Dentists in Miami, FL").
Extracts verified business details: Name, address, phone number, star rating, and website.
Deep-crawls each company's website to extract syntax-verified contact emails and social media profiles (LinkedIn, Instagram, Facebook).
Exports cleanly to CSV and JSON.

Best of all, you can run this completely free for your first 50 leads without needing a credit card or maintaining expensive residential proxy infrastructure.

The Architecture: Why Most Scrapers Fail

Traditional Google Maps scrapers just pull the visible DOM on Google Maps. That gives you:

Business Name: ✅
Phone Number: ✅
Physical Address: ✅
Website: ✅
Contact Email: ❌ (NOT stored on Google Maps)

To solve this, our pipeline deploys a 2-Stage Lead Enrichment Architecture:

┌────────────────────────┐      ┌────────────────────────┐      ┌────────────────────────┐
│  Stage 1: Google Maps  │ ───► │ Stage 2: Deep Website  │ ───► │  Stage 3: Data Export  │
│  Extract Listings &    │      │ Visit company site to  │      │ Clean CSV & JSON with  │
│  Official Website URLs │      │ scrape emails & socials│      │ verified B2B contacts  │
└────────────────────────┘      └────────────────────────┘      └────────────────────────┘

Rather than managing headless browsers, anti-bot bypasses, and proxy pools yourself, we wrap this pipeline into an open-source client calling the cloud Actor neon_innovation_lab/google-maps-b2b-lead-enricher.

Prerequisites

Python 3.8+ or Node.js 18+
A free Apify API token (Apify provides $5.00/month in free credits, and the first 50 leads incur $0 in custom event fees).
- Sign up free: https://console.apify.com/
- Copy your token from: https://console.apify.com/account/integrations

Method 1: Python Implementation

1. Install the SDK

pip install apify-client python-dotenv

2. The Python Script (`scrape_leads.py`)

import os
import csv
import json
from apify_client import ApifyClient

# 1. Initialize client with your free API token
client = ApifyClient(os.getenv("APIFY_TOKEN", "YOUR_APIFY_TOKEN_HERE"))

# 2. Define search parameters
run_input = {
    "searchQueries": ["Roofers in Austin, TX"],
    "maxResultsPerQuery": 50,         # First 50 leads are 100% free!
    "extractEmailsAndSocials": True,  # Deep website visit for emails & socials
    "countryCode": "us"
}

print("🚀 Starting Google Maps Lead Extraction with Email Enrichment...")
run = client.actor("neon_innovation_lab/google-maps-b2b-lead-enricher").call(run_input=run_input)

# 3. Retrieve dataset items
dataset_items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
print(f"🎉 Successfully scraped {len(dataset_items)} enriched leads!\n")

# 4. Export to CSV
with open("leads.csv", mode="w", newline="", encoding="utf-8") as f:
    writer = csv.writer(f)
    writer.writerow(["Title", "Category", "Phone", "Website", "Emails", "Rating", "Address"])
    for lead in dataset_items:
        emails = "; ".join(lead.get("emails") or [])
        writer.writerow([
            lead.get("title"),
            lead.get("category"),
            lead.get("phone"),
            lead.get("website"),
            emails,
            lead.get("rating"),
            lead.get("address")
        ])

print("💾 Saved to leads.csv!")

Run it with:

export APIFY_TOKEN="your_token_here"
python scrape_leads.py

Method 2: Node.js Implementation

1. Install the SDK

npm install apify-client dotenv

2. The Node.js Script (`scrape_leads.js`)

import fs from 'node:fs';
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN || 'YOUR_APIFY_TOKEN_HERE',
});

const runInput = {
  searchQueries: ['Dentists in Miami, FL'],
  maxResultsPerQuery: 50,        // First 50 leads are free!
  extractEmailsAndSocials: true, // Collect emails & social profiles
  countryCode: 'us',
};

console.log('🚀 Running lead extraction...');
const run = await client.actor('neon_innovation_lab/google-maps-b2b-lead-enricher').call(runInput);

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`✅ Extracted ${items.length} enriched businesses!`);

// Export to JSON
fs.writeFileSync('leads.json', JSON.stringify(items, null, 2));
console.log('💾 Saved to leads.json');

Sample Extracted Output

Here is an example of what the returned JSON dataset looks like:

{
  "title": "Austin Premier Roofing Co",
  "category": "Roofing contractor",
  "address": "1204 E 7th St, Austin, TX 78702",
  "phone": "(512) 555-0142",
  "website": "https://www.austinpremierroofing.com",
  "emails": [
    "contact@austinpremierroofing.com",
    "estimates@austinpremierroofing.com"
  ],
  "socials": {
    "linkedin": "https://www.linkedin.com/company/austin-premier-roofing",
    "facebook": "https://www.facebook.com/austinpremierroofing",
    "instagram": "https://www.instagram.com/austinroofingtx"
  },
  "rating": 4.9,
  "reviewsCount": 184,
  "googleMapsUrl": "https://www.google.com/maps/place/...",
  "query": "Roofers in Austin, TX",
  "scrapedAt": "2026-10-19T12:00:00.000Z"
}

Cost Comparison: Why Pay-Per-Event Wins

Tool	Monthly Subscription	Cost for 1,000 Leads	Free Trial
This Tool (`free-google-maps-lead-scraper`)	$0 / month	~$7.00	🎁 First 50 Leads Free
Outscraper	$29 – $99 / mo	$15 – $30	25 leads
PhantomBuster	$69 – $149 / mo	$40 – $80	14-day limit
Apollo / ZoomInfo	$99 – $1,000+ / mo	$150 – $300	Limited export

By leveraging Apify's Pay-Per-Event (PPE) model, you only pay for what you actually extract:

First 50 leads: $0.00 custom fee
Additional leads: $0.005 per enriched lead + $0.002 dataset storage
Total effective cost: ~$7.03 per 1,000 verified leads!

Open-Source GitHub Repository

The complete open-source code with CLI options (--query, --limit, --country), automated CSV/JSON formatting, and sample data is available on GitHub:

👉 GitHub: Ansarii/free-google-maps-lead-scraper

If you don't even want to run local code, you can trigger the scraper directly in your browser with 1 click:
👉 Run on Apify Store: google-maps-b2b-lead-enricher

Happy prospecting! If you found this useful, don't forget to star the GitHub repo! ⭐

Show HN: We scanned 23 public MCP servers for CVEs - Here is what broke

Neon Innovation Lab — Fri, 04 Sep 2026 11:21:33 +0000

With tens of thousands of Model Context Protocol (MCP) servers published in 2026, AI agents now have direct execution bridges to local terminals, filesystems, and production databases.

Following 30+ CVE disclosures against MCP tools in early 2026 alone (including CVSS 9.4+ RCEs), verifying the security of MCP servers before plugging them into Claude Desktop, Cursor, or autonomous agent runtimes has become critical.

However, existing scanning approaches suffer from two fatal flaws:

Active RCE on the Scanning Machine: Several popular scanners attempt to execute stdio commands directly from target configuration files to retrieve tool descriptions. If you scan an untrusted third-party server, you are executing arbitrary remote code on your own machine.
False-Positive Floods: Naive YARA or regex scanners trigger up to ~78% false-positive rates by flagging mock keys and test fixtures inside test/ folders.

To see what real-world security looks like, we built a zero-execution AST static auditor and benchmarked 23 prominent reference and community MCP servers.

Here is what we found.

The Audit Benchmark (23 Repositories)

We evaluated 23 MCP implementations across three tiers:

Anthropic Official Reference Servers (modelcontextprotocol/servers)
Archived Reference Servers (modelcontextprotocol/servers-archived)
Popular Community Implementations (fastmcp, slack-mcp-server, brave-search-mcp)

Executive Benchmark Table

#	Target MCP Server	Files Scanned	Trust Score	Grade	Flaws Found
1–7	Anthropic Core Reference (`filesystem`, `fetch`, `git`, `memory`, `sequentialthinking`, `time`)	~60	`100/100`	A+	0
8	Anthropic Reference (`everything`)	41	`85/100`	A	1 (Unauthenticated SSE)
9–20	Archived Reference Servers (`sqlite`, `postgres`, `slack`, `puppeteer`, `github`, `gitlab`, `redis`, `sentry`, `gdrive`)	~60	`100/100`	A+	0
21	FastMCP (Community)	491	`25/100`	F	4 (ZipSlip, 0.0.0.0 SSE, Unconstrained file tools)
22	Slack MCP Server	9	`100/100`	A+	0
23	Brave Search MCP	67	`100/100`	A+	0

What Actually Broke?

1. ZipSlip Archive Extraction (CWE-22)

In community telemetry handling, archive extraction was performed using tarfile.extractall() without safe directory containment or filter flags. In Python environments, unvalidated archive members can write arbitrary files outside the destination directory when unpacked.

The Fix: Always specify filter='data' in Python 3.12+ or sanitize archive member paths before extracting.

2. Unauthenticated Remote SSE Transports on `0.0.0.0` (CWE-306)

Multiple example scripts and server entrypoints bound Server-Sent Events (SSE) transports directly to 0.0.0.0 with no authentication middleware or Bearer token checks.

The Danger: Any client on the local network or public internet can discover the endpoint and execute MCP tool definitions without credentials.
The Fix: Default transport bindings strictly to 127.0.0.1 for local usage, and require Bearer token or OAuth authentication headers whenever binding to 0.0.0.0.

3. Missing Root Path Boundary Containment in File Tools (CWE-22)

We observed tools with functions named read_file or search_files that accept dynamic file path parameters from LLMs and pass them directly to open() without checking if the target path resides within an allowed root.

The Danger: An LLM encountering a prompt injection or untrusted input can easily traverse up directories (../../etc/passwd).
The Fix: Always enforce root boundary checks:

target = (ALLOWED_ROOT / user_path).resolve()
if not target.is_relative_to(ALLOWED_ROOT):
    raise PermissionError("Path traversal attempt detected")

Key Takeaways for Developers Building MCP Servers

Treat tool descriptions as untrusted context: Do not include hidden prompt overrides or unescaped user inputs in tool metadata.
Never invoke dynamic shells: Avoid subprocess.run(shell=True) and child_process.exec(). Always pass arguments as explicit string arrays.
Filter test fixtures from automated scanning: If you run security audits in CI/CD, exclude test mock directories to eliminate 90%+ of false-positive noise.

Run an Audit on Your MCP Servers

If you maintain an MCP server or use them in production, you can test your repository for these exact vulnerabilities without running untrusted code on your machine:

👉 MCP Security & Vulnerability Auditor on Apify Store

Drop in your public GitHub repository URL, and it generates a complete Trust Score (0–100) and actionable remediation report.

DEV Community: Neon Innovation Lab

2026 MCP Gateway Benchmark: Cutting Cursor and Claude Token Bloat by 85% with Zero-Trust AST Security

2026 MCP Gateway Head-to-Head Benchmark

Full Architecture & Custom Engineering

Best Free Google Maps Scrapers in 2026 (Outscraper Alternatives Compared)

Best Free Google Maps Scrapers in 2026 (Outscraper Alternatives Compared)

Quick Comparison Table

1. Free Google Maps Lead Scraper (Open-Source + Apify)

Why it stands out:

2. Outscraper

Pros:

Cons:

3. PhantomBuster

Pros:

Cons:

4. Apollo.io / ZoomInfo

Pros:

Cons:

Conclusion: Which Tool Should You Choose?

How to Scrape Google Maps Leads with Emails for Free (Python & Node.js)

How to Scrape Google Maps Leads with Emails for Free (Python & Node.js)

The Architecture: Why Most Scrapers Fail

Prerequisites

Method 1: Python Implementation

1. Install the SDK

2. The Python Script (scrape_leads.py)

Method 2: Node.js Implementation

1. Install the SDK

2. The Node.js Script (scrape_leads.js)

Sample Extracted Output

Cost Comparison: Why Pay-Per-Event Wins

Open-Source GitHub Repository

Show HN: We scanned 23 public MCP servers for CVEs - Here is what broke

The Audit Benchmark (23 Repositories)

Executive Benchmark Table

What Actually Broke?

1. ZipSlip Archive Extraction (CWE-22)

2. Unauthenticated Remote SSE Transports on 0.0.0.0 (CWE-306)

3. Missing Root Path Boundary Containment in File Tools (CWE-22)

Key Takeaways for Developers Building MCP Servers

Run an Audit on Your MCP Servers

2. The Python Script (`scrape_leads.py`)

2. The Node.js Script (`scrape_leads.js`)

2. Unauthenticated Remote SSE Transports on `0.0.0.0` (CWE-306)