# Superserve [Health: Active]

**Category:** 👨‍💻 Code Execution  
**Repository:** https://github.com/superserve-ai/superserve  
**GitHub Stars:** 469  
**Views:** 1  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/superserve

## Description
MCP server for Superserve sandboxes: create, exec, and manage Firecracker microVMs

## Claude Desktop Quick Installation
No confirmed install details yet, so no config is published. Follow the setup steps in the repository above.

## Documentation

## What Superserve does

Superserve exposes Superserve sandboxes to AI agents through the Model Context Protocol. The underlying sandboxes use Firecracker microVMs, providing isolated environments intended for persistent agent work. Through the server, an agent can create a sandbox, execute commands inside it, and manage the resulting microVM.

This fits workflows where an agent needs an execution environment rather than only text generation. Examples include running code, performing shell-based tasks, or keeping work in a sandbox across multiple operations. The server works with Superserve’s hosted sandbox platform; the repository also contains a console application, SDKs, documentation, and end-to-end tests.

## How it works

An MCP client connects an agent to the Superserve server, which translates tool calls into operations on Superserve sandboxes. The available operation groups are creation, execution, and management. A typical workflow starts by creating a Firecracker-backed sandbox, runs commands in that environment, and then uses management operations as needed during the sandbox’s lifecycle.

The project is organized as a Bun and Turborepo monorepo. JavaScript and TypeScript packages include the command-line interface and TypeScript SDK, while the Python side includes a `superserve` package managed through uv. The repository also includes a Next.js console and SDK end-to-end tests.

## Setup and configuration

Repository development uses both Bun and uv. Install the JavaScript and TypeScript dependencies with `bun install`, then install the Python workspace dependencies with `uv sync`. The available development commands include `bun run dev`, `bun run build`, `bun run lint`, `bun run format`, `bun run typecheck`, and `bun run test`.

The repository’s end-to-end test command accepts a `SUPERSERVE_API_KEY` value, for example `SUPERSERVE_API_KEY=ss_live_... bun run test:e2e`. That key is used for tests against staging. Applications can also use the published TypeScript SDK, `@superserve/sdk`, or the Python package, `superserve`.

## Tools and capabilities

- Create Superserve sandboxes backed by Firecracker microVMs.
- Execute commands inside a sandbox.
- Manage created sandboxes and their microVM lifecycle.
- Provide persistent sandbox environments for AI-agent workflows.
- Integrate with Superserve through TypeScript or Python SDK packages.

The repository links to Superserve’s documentation for the detailed API reference and platform-specific configuration.

_Full upstream README: https://allmcps.com/mcp/superserve/readme_