Query and Analyze Airtable Data from Slack—Using Just Natural Language

Many teams rely on Airtable as their central data hub—whether it’s for managing product backlogs, marketing campaigns, customer feedback, or sales pipelines. It’s flexible and powerful, but searching for and analyzing data often requires time-consuming manual work.

Runbear simplifies that process. By connecting Airtable via Model Context Protocol (MCP), you can build an AI agent that lets your team query, summarize, and analyze Airtable data directly from Slack—just by chatting. No complex setup, no coding. It works right where your team already communicates.

Ask Questions. Get Answers. All in Slack.

Let’s say you want to quickly summarize lead performance from a recent campaign. Instead of opening Airtable, filtering views, and downloading CSVs—you just type a message into Slack:

“Which campaign from last week had the highest email response rate?”

In seconds, your AI agent responds with:

• Campaign: Spring Launch 2025
• Leads Generated: 245
• Response Rate: 38.4%
• Insight: Email subject line A/B test showed Variant A had 2x higher click-through rate

This is a real example, powered by Runbear and connected to an Airtable MCP server. For marketers, PMs, and sales teams who regularly pull data—this turns hours of manual work into a simple one-line conversation.

Build Your Airtable Agent in Minutes

1. Create your Runbear account: Sign up and click “Create Assistant”, then choose “Build from scratch.”
2. Select Claude as your AI model: Choose “Anthropic Claude” and customize the system instructions using the template below.
3. Connect the Airtable MCP server: Add the Airtable MCP to enable your AI agent to access and query Airtable data.
4. Integrate with Slack: Connect your agent to your team’s Slack workspace in just a few clicks.

# Airtable Query Assistant

## Purpose
This assistant queries, summarizes, and analyzes Airtable data through Slack.

## Core Capabilities
- Search records based on user-defined filters
- Summarize numeric fields (sum, average, etc.)
- Filter by recent date ranges
- Sort and extract data by specific columns

## Technical Requirements
- Access Airtable via MCP server
- Base ID: [YOUR_AIRTABLE_BASE_ID]
- Target table: [YOUR_TABLE_NAME]
- Key fields: Campaign, Created Date, Response Rate, Tags, Owner

## Sample Response
"There were 132 new leads generated last week, with an average response rate of 21.3%. The top-performing campaign was 'Referral Drive' with a 41.5% response rate."

⚠️ Important: Make sure to customize the Technical Requirements section to reflect your actual Airtable setup. Replace table names, field names, and filter logic.

Put Your Airtable Data to Work—Faster and Smarter

With Runbear, your team can unlock the full value of your data without ever leaving Slack.

MCP makes it easy to connect not just Airtable, but also Google Sheets, Notion, HubSpot, and more—so you can build real, working automations in minutes.

Start building your first Airtable agent today with Runbear. It only takes a few minutes to experience a completely new way to work with data.