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Superserve

Create, execute commands in, and manage Superserve sandboxes backed by Firecracker microVMs.

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Install Superserve

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No confirmed install command yet

We only publish a config when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for Superserve, and we’d rather show nothing than a guess you’d paste into your client.

Open setup steps on GitHub

Overview

Superserve MCP server gives AI agents access to Superserve sandboxes backed by Firecracker microVMs. Its MCP tools cover sandbox creation, command execution, and sandbox management. Persistent, isolated environments make it suitable for agent workflows that need to run code or maintain sandbox state. The repository also provides TypeScript and Python SDKs for applications integrating with Superserve outside MCP.

Use cases

  • Create isolated environments for agent tasks
  • Execute commands inside Firecracker-backed sandboxes
  • Manage sandbox lifecycles from an MCP client
  • Run code in persistent agent workspaces

Key features

  • Firecracker microVM-backed sandboxes
  • Sandbox creation
  • Command execution
  • Sandbox management
  • TypeScript and Python SDKs

Security & health

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How Superserve works

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.

Read the full README View source on GitHub

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Frequently Asked Questions about Superserve

Superserve is an MCP server that connects AI agents to Superserve sandboxes backed by Firecracker microVMs. Its main tools create sandboxes, execute commands inside them, and manage their lifecycle. The platform is designed for persistent, isolated agent workspaces and also offers TypeScript and Python SDKs for application integrations.

Install the repository dependencies with `bun install` and `uv sync`. The project uses Bun workspaces, Turborepo, and uv workspaces. The repository also publishes `@superserve/sdk` for TypeScript and `superserve` for Python integrations.

Run `SUPERSERVE_API_KEY=ss_live_... bun run test:e2e` to execute the end-to-end tests against staging. Unit tests that do not require credentials can be run with `bun run test`.

Superserve sandboxes can be created, used to execute commands, and managed through the MCP server. They run on Firecracker microVMs and support persistent, isolated environments for AI-agent workflows.

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