Kernel MCP Server

The Kernel MCP Server is a cloud‑hosted bridge that brings the full power of the Kernel platform into any Model Context Protocol (MCP)‑compatible AI assistant. By exposing a secure, authenticated API surface, the server lets assistants deploy and manage Kernel applications, launch headless Chromium sessions for web automation, monitor deployment health, search the rich Kernel documentation graph, and even evaluate JavaScript snippets while streaming live DOM snapshots. This eliminates the need for developers to build custom integrations, providing a single point of entry that can be consumed by Claude, Cursor, Goose, and any other MCP‑aware client.

For developers building AI‑powered workflows, the server’s OAuth 2.0 authentication via Clerk guarantees that only authorized users can access their Kernel resources, keeping sensitive data and credentials safe. The remote MCP endpoint () is fully managed, so teams can focus on feature development instead of maintaining a persistent server. The ability to use streamable HTTP or stdio transports gives clients flexibility, while dynamic client registration simplifies onboarding and revocation of access.

Key capabilities include:

• App lifecycle management – deploy, update, and scale Kernel apps directly from the assistant.
• Browser automation – spin up headless Chromium sessions, navigate pages, and capture snapshots in real time.
• Observability – retrieve deployment metrics and invocation logs to monitor performance or troubleshoot issues.
• Context injection – search Kernel’s documentation graph and inject relevant information into the assistant’s prompt.
• Code evaluation – run arbitrary JavaScript and stream the resulting DOM, enabling dynamic UI generation or data extraction.

Typical use cases span from automating routine DevOps tasks—such as rolling out new microservices—to building interactive research assistants that can browse the web, scrape data, and present visualizations—all without leaving the AI chat. In a product‑release pipeline, an engineer can ask the assistant to “deploy version 2.1 of my API and run smoke tests,” and the server will handle the entire deployment cycle, return status updates, and surface any failures. In a data‑analysis scenario, a researcher might instruct the assistant to “search for recent articles on climate change in the Kernel docs and summarize key findings,” with the server fetching and contextualizing the relevant content.

By centralizing access to Kernel’s tooling under a single, secure MCP endpoint, the server offers a compelling advantage: developers can prototype and iterate on AI‑driven workflows instantly, without spinning up infrastructure or writing custom adapters. This plug‑and‑play model accelerates time to value, ensures consistent security practices, and unlocks the full breadth of Kernel’s cloud‑native capabilities for any MCP‑compatible AI assistant.