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genpark-agentic-cold-email-outreach-spam-score-guard-skill

mcp
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SUMMARY

AI B2B Cold Email Outreach Spam Score & Deliverability Guard. Analyzes email copy against SpamAssassin keyword heuristics, exclamation/caps ratios, unsubscribe compliance, and reading ease grade levels to maximize primary inbox placement. | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-agentic-cold-email-outreach-spam-score-guard-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

AI B2B Cold Email Outreach Spam Score & Deliverability Guard. Analyzes email copy against SpamAssassin keyword heuristics, exclamation/caps ratios, unsubscribe compliance, and reading ease grade levels to maximize primary inbox placement.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import AgenticColdOutreachDeliverabilityGuard

def main():
    guard = AgenticColdOutreachDeliverabilityGuard()
    s = "ACT NOW! 100% FREE GROWTH TOOL!!!"
    b = "Click here to make money guaranteed! Limited time special promotion!"
    res = guard.evaluate_email_deliverability(s, b)
    print("--- Email Deliverability Audit ---")
    print(f"Spam Score: {res['composite_spam_score']}/10 | Tier: {res['deliverability_tier']}")
    print(f"Flags: {res['triggered_spam_keywords']}")
    print(f"Recommendation: {res['recommendation']}")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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