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Alpha-Park

Waterfall Enrichment Orchestrator

by Alpha-Park

GenPark AI Agent Skill - Waterfall Enrichment Orchestrator

Zero-dependency Python agent skill providing multi-tiered waterfall data enrichment for B2B prospects, verified decision-maker corporate emails, and firmographic telemetry.

Verified by GenPark AI and compatible with Model Context Protocol (MCP).

Architecture Diagram

graph TD
    A[Incoming Prospect Query] --> B[Waterfall Enrichment Orchestrator]
    B --> C{Primary Graph Hit?}
    C -->|Yes| D[Parse Firmographics & Direct Work Email]
    C -->|No / Incomplete| E{Secondary Exchange Hit?}
    E -->|Yes| F[Fill Missing Tech Stack & Pattern Email]
    E -->|No| G[Tertiary Registry Fallback]
    D --> H[Email Syntax & MX Deliverability Filter]
    F --> H
    G --> H
    H --> I[Unified High-Confidence Prospect Record]

Related MCP server: Signaliz MCP

Features

  • Deterministic Multi-Provider Cascade: Eliminates single data source failure with automated fallback.

  • Strict Business Email Heuristics: Distinguishes corporate domains from disposable and freemail providers.

  • Zero External Dependencies: Standard Python 3.9+ library implementation.

  • MCP Server Compatibility: Seamless integration with LLM agent tool calling via standard JSON-RPC.

Quickstart

from client import WaterfallEnrichmentOrchestrator

orchestrator = WaterfallEnrichmentOrchestrator()
prospect = orchestrator.enrich_prospect({
    "first_name": "Alex",
    "last_name": "Chen",
    "title": "CTO",
    "domain": "acmesystems.io"
})
print(prospect["target_contact"]["email"])

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