VP of engineering Jackie Brosamer and principal engineer Brad Axen join me to demo Goose, Block’s open-source AI agent that runs locally, plugs into your existing tools through model context protocol (MCP) servers, and peels away the rote parts of work so people can focus on insight and impact.

This episode is packed with in-depth demos: starting with a messy farm-stand sales CSV, Goose analyzes the data, builds visualizations, and generates a shareable HTML report. We then spin up an MCP that lets Goose talk to Square’s dashboard for inventory management, vibe code an email MCP that can send payment links automatically, and unpack how environment setup, debugging, and tool orchestration get handled behind the scenes.

What you’ll learn:

1. A practical, repeatable workflow for turning any working script or function into a custom MCP—and exposing it to natural-language control
2. How to transform messy CSVs into visualizations, HTML reports, and actionable business insights without needing a data science background
3. Ways to hook Goose into live business systems (e.g. Square inventory, payments) so analysis flows directly into operational action
4. The thinking behind Block’s decision to open-source Goose
5. Lessons from Block’s bottom-up meets top-down adoption model
6. Why organizational transformation, not just picking the right LLM, will separate AI winners from laggards over the next few years
7. How to scale an internal MCP catalog
8. The organizational transformation required to fully leverage AI capabilities

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Where to find Jackie Brosamer:

LinkedIn: https://www.linkedin.com/in/jbrosamer/

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Where to find Brad Axen:

LinkedIn: https://www.linkedin.com/in/bradleyaxen/

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Where to find Claire Vo:

ChatPRD: https://www.chatprd.ai/

Website: https://clairevo.com/

LinkedIn: https://www.linkedin.com/in/clairevo/

X: https://x.com/clairevo

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In this episode, we cover:

(00:00) Introduction to Goose and its data analysis capabilities

(02:27) How Block embraced AI across the organization

(04:48) What Goose is and why Block open-sourced it

(07:45) Demo: Analyzing farm-stand sales data with Goose

(12:18) Creating shareable HTML reports from data analysis

(14:15) Model context protocols (MCPs) that Goose uses

(18:56) Demo: Using Square MCP to create a product catalog

(23:35) Creating payment links from analyzed data

(26:30) Demo: Building a custom email MCP

(31:18) Testing the new email MCP with Goose

(36:09) Debugging and fixing MCP code errors

(38:44) Connecting workflows: sending payment links via email

(41:30) Lightning round and final thoughts

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Tools referenced:

• Goose: https://block.github.io/goose/

• Pandas: https://pandas.pydata.org/

• Plotly: https://plotly.com/

• Python: https://www.python.org/

• ChatGPT: https://chat.openai.com/

• Claude: https://claude.ai/

• Cursor: https://www.cursor.com/

• Mailgun: https://www.mailgun.com/

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Other references:

• Block: https://block.com/

• Model context protocol (MCP): https://www.anthropic.com/news/model-context-protocol

• GitHub: https://github.com/

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