MCP Sandbox

Overview

MCP Sandbox transforms any JavaScript module into a fully‑functional Model Context Protocol (MCP) server with zero manual configuration. By automatically reflecting on a module’s exported functions, it generates MCP‑compatible tool definitions, type schemas, and documentation. The result is a secure, sandboxed environment where AI assistants can discover, invoke, and monitor JavaScript logic through standard JSON‑RPC 2.0 or Server‑Sent Events (SSE) channels, while developers retain the convenience of legacy REST endpoints for quick testing.

Solving a Common Integration Gap

When building AI‑powered applications, developers often need to expose custom business logic or utility functions to the assistant. Traditional approaches require writing adapters, maintaining manual type contracts, and ensuring sandboxed execution—tasks that are error‑prone and time‑consuming. MCP Sandbox eliminates these pain points by automating reflection, type inference from defaults and naming conventions, and JSDoc extraction. The server also enforces strict VM isolation with configurable timeouts, protecting the host system from malicious or runaway code.

Key Features and Value

• Automatic Reflection – Scans module exports to discover function signatures without developer intervention.
• Secure Sandboxing – Runs each tool in an isolated VM context, preventing accidental leakage of state or resources.
• Smart Type Inference – Generates JSON schema for parameters, enabling strong typing and IntelliSense in IDEs.
• JSDoc Integration – Pulls descriptive comments into the MCP tool metadata, improving discoverability for AI assistants.
• Full MCP Support – Exposes JSON‑RPC 2.0 endpoints, SSE streams for real‑time updates, and a lightweight REST API for quick prototyping.
• TypeScript Compatibility – Provides type safety and autocomplete when consuming the sandbox programmatically.

Real‑World Use Cases

• Rapid Prototyping – Quickly expose utility libraries (e.g., math helpers, data transformers) to an assistant during early development.
• CI/CD Pipelines – Run tests or linting tools through MCP, allowing an AI to report issues directly in a chat interface.
• Microservice Orchestration – Treat each sandbox as a micro‑tool that can be composed by higher‑level assistants to build complex workflows.
• Education & Demo Environments – Demonstrate how JavaScript functions can be safely leveraged by AI, with live SSE updates showing execution progress.

Integration into AI Workflows

Developers can launch MCP Sandbox alongside their existing backend services. An AI assistant then queries the endpoint to list available operations, calls them via the or channels, and receives structured responses. Because the server outputs JSON schema for each tool, client libraries can automatically generate type‑safe wrappers, reducing boilerplate and potential runtime errors. The optional REST API further lowers the barrier for quick experimentation or integration into legacy tooling that expects HTTP endpoints.

Unique Advantages

MCP Sandbox’s blend of automatic reflection, strong typing, and secure sandboxing sets it apart from generic RPC frameworks. It is purpose‑built for the Model Context Protocol, ensuring that every exported function becomes a first‑class tool with minimal overhead. The ability to run arbitrary JavaScript safely while exposing rich metadata makes it an ideal bridge between conventional codebases and next‑generation AI assistants.