mcp server
gtm-engineering-playbook
Systems thinking for GTM engineers. Automation lifecycle, enrichment waterfalls, knowledge graphs, MCP patterns, structured uncertainty.
Set up
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This project does not publish an install method we can turn into a config. Follow the README instead.
README
synced Oct 23, 2026GTM Engineering Playbook
A systems thinking playbook for engineers who build and operate go-to-market automation.
This is not a tutorial on which buttons to click in HubSpot. It is a collection of engineering principles that make GTM systems reliable, trustworthy, and scalable. The patterns here come from building and operating 20+ production automations across outbound campaigns, data enrichment pipelines, AI agent workflows, and monitoring infrastructure.
Who This Is For
- GTM Engineers building automated outreach, enrichment, and campaign systems
- RevOps and Marketing Ops teams moving from visual builders to code-based automation
- Sales Ops teams designing data pipelines for lead scoring and routing
- Anyone operating AI-powered GTM workflows who wants them to work reliably
What This Covers
| Document | Core Question |
|---|---|
| Automation Lifecycle | How do automations earn trust over time? |
| Retrieve Before Generate | Why should every system check what exists before creating something new? |
| Knowledge Graph Architecture | How do you structure operational intelligence so it compounds? |
| Structured Uncertainty | How do you build systems that surface their own blind spots? |
| Enrichment Waterfall Design | How do you design multi-source data enrichment that balances cost and reliability? |
| MCP Server Patterns | When and how should you give AI agents structured access to external data? |
| Operational SOPs | How do you write procedures that both humans and automated systems can follow? |
Templates
| Template | Purpose |
|---|---|
| Automation Design | Plan a new automation before writing code |
| Enrichment Waterfall | Design a multi-source enrichment pipeline |
| SOP Template | Write a new standard operating procedure |
The Core Insight
GTM engineering is not about tools. Tools change every year. The principles underneath them do not.
A well-designed enrichment pipeline works the same way whether you run it through Clay, a Python script, or a Trigger.dev task. A good monitoring strategy applies whether your automations live in Zapier, n8n, or custom code. The question is never "which tool?" The question is "what are the failure modes, and how do I catch them before they matter?"
This playbook captures those principles. Each document explains a pattern, why it exists, and how to implement it in practice.
How to Use This
Read whatever is relevant to what you are building right now. These documents are designed to stand alone. If you are:
- Starting a new automation: Read Automation Lifecycle and use the Automation Design Template.
- Building an enrichment pipeline: Read Enrichment Waterfall Design and use the Enrichment Waterfall Template.
- Wiring AI agents to data sources: Read MCP Server Patterns.
- Setting up knowledge management for your GTM team: Read Knowledge Graph Architecture and Retrieve Before Generate.
- Scaling from 1 automation to 20: Read Operational SOPs and Structured Uncertainty.
License
MIT. See LICENSE.