1 min lesson
Where non-coders actually work: the Agents window
Describe the practical point in "Check Docs > Agent > Agents Window for the current path in your build", then say what it changes.
Step 1 of 3
When asked how you'd approach building internal GTM tooling, skip the platform roadmap. Say: "I'd solve one painful step, consolidating the prospecting data with agent-written SQL, ship it, let reps eyeball and correct the output, then add scoring and drafting as demand pulls them in." Incremental, demand-led work with a human in the loop sounds like someone who has shipped this.
Learn more
Advanced table
MCPs to your internal knowledge bases
MCPs to your internal knowledge basespersonal vs team servers
MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. servers are how Cursor's team connects the agent to their internal knowledge bases. A teammate in the demo splits personal MCPs, such as Figma and Granola, from team MCPs: Databricks for the database, Statsig to read every experiment and backend services. When the agent got confused mid-run, they fixed it with a single instruction: "use the Databricks MCP." The catalog runs well past coding tools.
- MCP
- Databricks
- What it connects
- The team database: schema, user and usage data
- Scope
- Team
- MCP
- Statsig
- What it connects
- Reads through all experiments
- Scope
- Team
- MCP
- Figma
- What it connects
- Design files and design-system context
- Scope
- Personal
- MCP
- Granola
- What it connects
- Meeting notes and transcripts
- Scope
- Personal
- MCP
- Slack
- What it connects
- Fetch context and post messages
- Scope
- First-party / marketplace
- MCP
- Notion
- What it connects
- Internal knowledge base
- Scope
- First-party / marketplace
- MCP
- Datadog
- What it connects
- Alerts and monitoring
- Scope
- First-party / marketplace
| MCP | What it connects | Scope |
|---|---|---|
| Databricks | The team database: schema, user and usage data | Team |
| Statsig | Reads through all experiments | Team |
| Figma | Design files and design-system context | Personal |
| Granola | Meeting notes and transcripts | Personal |
| Slack | Fetch context and post messages | First-party / marketplace |
| Notion | Internal knowledge base | First-party / marketplace |
| Datadog | Alerts and monitoring | First-party / marketplace |
Many marketplace integrations are coding-flavored, but PM, design, writing and support ones exist too.
Explore once, then 'create a skill'pay the discovery cost once
The same teammate gave the agent almost no schema and let it burn tokens learning the database structure through the Databricks MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition.. Then: "create a skill for how to fetch user data and creation date." On the re-run they invoked it explicitly, "use skill," so the agent does not crawl the whole database again and knows exactly where to look. Pay the discovery cost once, and every future run is fast and deterministic.
"Once it learns the structure and figures out how to work, I'll ask it to create a skill to do this. Now it runs a ton faster. Instead of looking through the entire database, it uses the skill and knows exactly where to look."
Meeting recaps and always-on automationsfrom one-off to scheduled
A directly copyable recipe: connect Granola via MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. and prompt for a weekly recap of the past 7 days of meeting notes, plus an action-item list of 'things I promised to do' so you stop getting pinged. The agent lists meetings, queries notes and returns a summary in seconds. It extends to Google Calendar. The output is concrete: external calls, a growth sync, a GTM-product discussion and action items like 'create a community-program application form.'
Turn that one-off into an Automation, an always-running agent on a schedule such as every Monday, or on a trigger such as a GitHub pull request opened, a Slack channel created or a new message in a watched channel. Automations default to memory, so they look at prior runs, and you attach connectors inline. Slack and cron triggers default to no repository, so a Granola recap needs no GitHub connection.
A bug-report triage agent is a second pattern worth copying. It files reports to Linear and dedupes them, upvotes when many people flag the same thing and follows up asking for screenshots and repro steps.
Learn more
Full explanation
Full explanation
Know the real constraints before you promise a stakeholder a scheduled agent. Source-control triggers need a repository, Slack triggers see only public channels and Automations are a Premium-and-above feature.
QCursor's team built ChatGTM, an internal prospecting tool. What was their actual build approach, and why would an interviewer value it?
Learn more
Optional practice
Test yourself on Where non-coders actually work: the Agents window
QYou build a weekly Granola meeting-recap Automation that runs every Monday and posts to Slack. Does it need a connected GitHub repository?