Waterfall Enrichment Orchestrator
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Waterfall Enrichment OrchestratorEnrich prospect: Alex Chen, CTO at acmesystems.io"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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"])This server cannot be deployed
Maintenance
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