How We're Using MCP to Automate Real Workflows: 6 Working Use Cases
Six practical use cases you can deploy this week.
Since early 2025, Model Context Protocol (MCP) has been gaining serious traction among teams building AI agents. It gives LLMs structured, secure access to real-world tools—like Slack, Google Calendar, Notion, BigQuery, and more—without the need for custom integration work.
At Runbear, we’ve spent the past few weeks experimenting with public MCP servers to improve our own internal workflows. We’ve also learned a lot from customers who are using MCP to bring AI deeper into their operations.
In this post, I’m sharing six real, working use cases—some we’ve built ourselves, others inspired by customers—that show how MCP-powered agents are already saving time, reducing friction, and helping teams stay in sync. If you’re building with LLMs or exploring how AI can actually support your day-to-day work, chances are one of these ideas could work for you too.
1. Meeting Scheduling with Google Calendar + Google Meet MCP Servers
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Problem: Scheduling meetings across multiple calendars and rooms wastes time and often leads to back-and-forth messages.
Solution: We built an agent that:
- Checks everyone's availability via the Google Calendar MCP server
- Finds an open meeting room
- Creates a meeting with a Google Meet link—all from Slack
Impact: Meetings get scheduled in seconds, not minutes. No more context-switching or calendar juggling.
2. Natural Language Analytics with BigQuery MCP Server
Problem: Non-technical team members struggle to get quick insights from complex datasets.
Solution: We connected the BigQuery MCP server to Slack so anyone can ask questions like:
“How did our March LinkedIn ads perform compared to February?”
Impact: Faster decisions, fewer data bottlenecks, and more self-sufficient teams.
3. Slack Summarization for Docs & Postmortems
Problem: Important discussions and decisions in Slack often slip through the cracks—especially in fast-paced teams.
Solution: By connecting the Slack and Google Docs MCP servers, an AI agent can:
- Monitor relevant channels or threads
- Summarize key conversations and decisions
- Automatically create a well-structured document in Google Docs—for things like postmortems, meeting summaries, or strategy alignment
Impact: Teams get clear, shareable documentation without any manual effort.
And because MCP works across tools, this same workflow can be adapted to Notion, Confluence, or wherever your team prefers to write.
4. Daily Check-Outs via Linear + GitHub MCP Servers
Problem: Remote teams need a lightweight way to stay aligned without daily meetings.
Solution: Our agent gathers:
- Updates from Linear
- Commits and PRs from GitHub
- Then posts a daily summary in Slack
Impact: Everyone stays in sync across time zones, without interrupting deep work.
5. Pre-Meeting Briefings
Problem: Sales and CS teams waste time digging through emails and CRM notes before client meetings.
Solution: An agent uses the HubSpot and Gmail MCP servers to:
- Summarize recent conversations
- Highlight open action items
- Generate a quick meeting prep brief
Impact: Better meetings, faster follow-ups, and less prep time.
6. Personal Daily Slack Digest with Slack MCP Server
Problem: It’s easy to miss key Slack messages, especially in busy channels or async teams.
Solution: A personal daily digest agent powered by the Slack MCP server:
- Reviews mentions and threads from the previous day
- Summarizes important messages
- Sends a personalized recap every morning
Impact: No more FOMO. Everyone starts their day informed and ready to go.
Start Building with MCP
These aren’t just ideas—they’re real, working workflows powered by MCP. Whether you’re automating meetings, surfacing insights from Slack, or turning customer history into meeting prep, MCP helps AI agents plug into the tools your team already uses—with minimal setup and maximum impact.
If you’re exploring how AI can truly support your team’s day-to-day work, now’s a great time to try MCP for yourself!
