dagger/container-use

Overview

The container‑use MCP server gives AI coding assistants a robust, isolated playground in which to work. By spawning a fresh Docker container for each agent and tying that environment to a dedicated Git branch, developers can run multiple agents in parallel without the risk of one agent overwriting or contaminating another’s work. This separation is invaluable when experimenting with new libraries, trying out different language versions, or debugging complex build pipelines—any failure is confined to its own container and can be discarded with a single command.

At the core of container‑use is a set of practical capabilities that make it a natural fit for modern AI workflows. Agents can request a container, run arbitrary commands, and receive logs and command history in real time, allowing the host to audit exactly what was executed. The server exposes a simple interface that can be attached to any MCP‑compatible agent, such as Claude Code or Cursor, enabling seamless integration without modifying the agent itself. Developers can also drop into an agent’s terminal whenever it stalls, inspect its state, and intervene manually—bridging the gap between automated reasoning and human oversight.

Key features include:

• Isolated, branch‑scoped environments – each agent gets its own container and Git branch, so work is reproducible and easily roll‑backed.
• Live visibility – command history and logs are streamed to the host, providing transparency into an agent’s actions.
• Direct terminal access – agents can be paused and a user can interact with the container in real time.
• Git‑first workflow – review or merge an agent’s changes by simply checking out the associated branch.
• Vendor‑agnostic – any MCP server can be paired with container‑use, making it a drop‑in solution for diverse tooling stacks.

These strengths translate into several real‑world scenarios. In a team setting, multiple developers can delegate code generation tasks to separate agents that run in their own containers, ensuring that experimental features do not interfere with the main codebase. In continuous integration pipelines, an agent can build and test a new dependency in isolation, reporting failures back to the CI system without affecting other jobs. Finally, educators can let students experiment with code generation in a sandboxed environment where mistakes are harmless and instantly reset.

By providing isolated, auditable, and easily controllable execution contexts, container‑use empowers AI assistants to work safely at scale. It removes the friction of environment setup, eliminates cross‑agent contamination, and gives developers a clear view into every step an agent takes—all while remaining fully compatible with existing MCP workflows.