Advanced

You are using an MCP server designed to give AI coding agents a secure, flexible environment for executing coding tasks. It manages sessions, resources, tool execution in sandboxed runtimes, and policy enforcement so you can collaborate across agents with auditable actions and scalable tooling.

How to use

Start the server in development mode to enable interactive sessions and sandboxed tooling. After the server is running, you can configure your MCP client to connect over HTTP or run the server locally as a stdio process to integrate directly into your tooling workflow.

Typical usage patterns include initializing a session with the tools you plan to use, negotiating capabilities, and running coding tasks within sandboxed environments. Use the session to perform file operations, run commands, analyze code, and generate documentation while policies ensure access control and auditability.

How to install

Prerequisites include Node.js 18+ and optional Docker for enhanced sandboxing.

Install and run the server locally with these steps.

# Install dependencies after cloning the project
npm install

# Run the development server locally
npm run dev

# Optional: start with Docker for sandboxing
docker-compose up

Additional sections

Configuration and security are designed to keep your environment secure and auditable. The server supports session management, resource policies, rate limiting, and an immutable audit log to track all actions.

Examples of workflows include starting the server, initializing a session with available tools, and using a client like Qwen Code to perform secure operations inside sandboxed runtimes. You can configure a client to point at the MCP server URL and begin sessions with your preferred tooling.

Available tools

Session management

Controls authentication, session lifecycle, and capability negotiation to ensure secure interactions.

Resource management

Provides a file system abstraction with policy enforcement to govern access and usage of resources.

Sandboxed tool execution

Runs commands and tools inside isolated Docker-backed sandboxes with controlled resources.

Policy engine

Enforces access rules, audits actions, and governs how agents can interact with tools and data.

Multi-agent collaboration

Supports coordinated work among multiple AI agents within secure sessions.

Extensible tooling

Allows integration of additional tools for code analysis, testing, and documentation.

Rate limiting

Controls request throughput to protect resources and ensure fair usage.

Custom tools

Includes capabilities for code analysis, testing, and documentation generation.