We're launching a public MCP server that gives AI agents direct access to Airbyte's complete knowledge base. This includes our documentation, API specifications, GitHub issues/PRs, YouTube content, and community discussions.
What's included The Airbyte Knowledge MCP aggregates information from:
Documentation : Complete technical docs covering setup, connectors, and deploymentOpenAPI specs : Programmatic access to our API structureGitHub : Issues, discussions, and pull requests across our repositoriesYouTube : Tutorial videos and product demosWebsite content : Product information and use casesThis gives agents reliable, up-to-date information about how Airbyte works rather than relying on potentially outdated training data.
How to use it For Claude Desktop or Claude Code, run:
bash
claude mcp add --transport http airbyte-docs
https://airbyte.mcp.kapa.ai
If you're using Cursor or VS Code, you can connect with one click through the Ask AI button in our documentation .
The server works with any MCP-compatible tool including Windsurf and ChatGPT Desktop. The server URL is https://airbyte.mcp.kapa.ai.
When you first connect, you'll authenticate with a Google account (used only for rate limiting, we don't access your personal data). Each user gets 40 requests per hour and 200 per day.
Why we built this We've been using AI agents internally at Airbyte for development workflows, customer support, and documentation maintenance. We kept running into the same problem: agents would hallucinate outdated information or miss recent changes to our platform.
Building our own MCP server solved this. Now our agents—and yours—can query fresh information directly from our documentation and community resources.
Use cases The Airbyte Knowledge MCP is particularly useful for:
Building custom connectors : Access code examples and best practices from our GitHub reposAPI integrations : Query our OpenAPI specs and implementation guidesTroubleshooting : Search through GitHub issues and discussions for similar problemsLearning Airbyte : Get agents up to speed on our architecture and deployment modelsWe're already using it internally with our development agents. If you're building agents that interact with Airbyte, give it a try.
Find the full documentation here .
