Sandbox MCP
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- AlicenseAqualityBmaintenanceEnables AI coding agents to safely execute code, run tests, and build projects inside disposable Docker sandboxes, protecting the host machine through enforced isolation, filesystem snapshots, and network controls.15MIT
- AlicenseAqualityBmaintenanceEnables AI coding agents to safely run commands, install dependencies, test, and modify code inside disposable, policy-enforced Docker sandboxes isolated from the host, returning results and diffs.15MIT
- FlicenseNot gradedqualityCmaintenanceEnables AI agents to safely execute Python, JavaScript, and Bash code in an isolated Docker sandbox with strict security constraints.1-
- AlicenseNot gradedqualityBmaintenanceProvides a restricted Docker-based sandbox for LLM agents, enabling file operations, command execution, and local Git within an isolated runtime.76 npmMIT
- FlicenseNot gradedqualityCmaintenanceProvides AI coding agents with a secure, sandboxed environment for executing coding tasks including file operations, command execution, and testing. Features session management, policy enforcement, and Docker-based sandboxing for safe code execution and development workflows.-
- FlicenseAqualityCmaintenanceEnables AI coding agents to run Kubernetes inspection and Terraform plan/apply operations inside ephemeral gVisor-sandboxed jobs with short-lived, narrowly-scoped credentials, while routing destructive changes through a human approval gate.3-
TDQS
Scored across 15 tools
Each tool maps cleanly to a distinct lifecycle phase: create, execute, test, file operations, change inspection, artifact collection, job management, and teardown. Even execute_experiment versus run_tests is explicitly delineated, so there is no real ambiguity in choosing between tools.
All names follow a consistent snake_case verb_noun pattern with predictable verbs like create, destroy, execute, get, list, read, write, cancel, and compare. The object nouns are similarly stable, making the toolset easy to navigate and predict.
Fifteen tools sit at the upper edge of a well-scoped server, but every tool has a distinct purpose and none feels redundant. The background job helpers, runtime check, and comparison tool all earn their place in the sandbox workflow.
The toolset covers the full sandbox experiment lifecycle: create, run commands and tests, read and write files, inspect changes, collect artifacts, compare experiments, retrieve state, and destroy. It also includes job polling and a runtime health check, leaving no obvious dead ends for agent workflows.