How I AIHow this PM uses MCPs to automate his meeting prep, CRM updates, and customer feedback synthesis
CHAPTERS
- 0:00 – 2:41
MCPs demystified: app connectors for your AI tools
Claire and Reid open by reframing MCPs in plain language: they’re essentially app integrations/connectors that let AI tools both read knowledge from your apps and take actions inside them. They outline the two core motivations—accessing context and executing work—setting up the practical demos that follow.
- 2:41 – 4:05
Zapier’s MCP approach: 8,000 apps and tool collections per client
Reid explains how Zapier exposes a massive library of searches/actions via MCP and why Zapier’s server approach is more like a platform for building multiple servers. The key idea is creating curated toolsets tailored to a specific AI client or agent so the model has the right capabilities without excess access.
- 4:05 – 9:00
Configuring tools responsibly: scoping access and reducing tool confusion
They discuss practical configuration details: restricting which databases, notebooks, or records an AI can touch and why that improves safety and reliability. Claire highlights a common pain point—tool selection conflicts—arguing that better prioritization and control will be increasingly important as MCP adoption grows.
- 9:00 – 12:05
Claude Projects as a tool-usage “playbook” for MCP workflows
Reid shares a key tactic: using Claude Projects not just for knowledge, but for detailed operational instructions on how tools should be used, in what order, and how data should be written back. This turns a general-purpose model into a more consistent operator for repeated workflows like CRM updates.
- 12:05 – 15:25
Daily meeting prep automation: calendar scan + CRM/company intelligence
Reid describes a daily planning workflow that reviews his calendar and automatically enriches each meeting with context—who the person is, company usage, prior interactions, and other internal data. The goal is to stop showing up to meetings without context and to help customer-facing teams be better prepared by default.
- 15:25 – 18:15
Post-meeting notes management: from Granola notes to structured records
They walk through Reid’s pain point: post-meeting note logging is tedious and easy to skip. Using MCP-enabled tools plus project instructions, Claude can check for existing records, search internal sources (like Glean/Slack knowledge), and create or update structured entries in systems like Coda and HubSpot.
- 18:15 – 20:04
Agentic instructions vs deterministic Zaps: brittleness, speed, and “where you work”
Claire contrasts her deterministic Zapier workflow builder approach with Reid’s agentic, natural-language instruction approach in Claude. Reid explains trade-offs: agentic flows can be more flexible but may hit time limits, while deterministic workflows handle long-running steps better; the big win for MCPs is bringing actions into the AI tools people already use.
- 20:04 – 23:10
Idea jammer: using MCP tool access to challenge and refine concepts
Reid briefly shares an “idea jammer” Claude Project wired to his tables and references. The project helps him explore whether ideas were tried before, where they might fit, and applies prompting methods to challenge assumptions—showing MCP value beyond pure admin work.
- 23:10 – 25:05
Customer interview prep workflow: Databricks lookup + Coda briefing page
For interview preparation, Reid relies on a more deterministic workflow because the underlying research lookup can take time. The flow pulls enriched customer/company context (e.g., from Databricks), summarizes it, and appends it to a Coda page so he can enter interviews fully informed—even when the lead came from loosely tracked sources like LinkedIn.
- 25:05 – 29:16
Why Gemini is in the loop: file-native processing for PDFs/HTML
They unpack a model-choice nuance: Gemini performs especially well on file-based inputs. Reid’s data output comes as HTML/PDF-like artifacts, so he converts HTML to a file to reduce tokens and improve processing quality, then uses Gemini in the workflow to interpret and summarize the document reliably.
- 29:16 – 31:48
Creating a virtuous cycle: turning support tickets and transcripts into FAQs
Reid describes a system that keeps customer-facing knowledge fresh without constant manual upkeep. Each closed support ticket or chatbot transcript is analyzed to extract the core question and solution; if it’s missing from the knowledge base, the system proposes a new FAQ entry for human review and then publishes it to the bot’s source database.
- 31:48 – 33:03
“Run ChatGPT in your sleep”: a framework for brainstorming high-leverage automation
Claire and Reid share mental models for finding strong AI use cases: imagine an infinite, perfect team or imagine running AI while you sleep. The point is to move beyond “do the same work faster” toward “raise quality and coverage,” like systematically reviewing every support interaction to improve documentation.
- 33:03 – 37:16
Personal workflows: family calendar from a photo + kid music with Suno
In the lightning round, Reid shares personal automations that deliver real-life value. He uses a Claude Project plus Zapier MCP to translate photos of a physical family calendar into correctly blocked Google Calendar events (including travel buffers), and he uses Claude + Suno to generate custom kids’ songs—both for fun and to teach kids prompt specificity.
- 37:16 – 40:28
NotebookLM interview prep: personalized audio briefings for a job search
Reid closes with a high-impact personal use case: preparing his wife for interviews using NotebookLM. He gathers company/job materials, generates an “audio overview” tailored to her background, and she listens before interviews—improving her readiness and perceived domain knowledge.
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