Zig

You run a Zig MCP Server that adds Zig language tooling, code analysis, and documentation access to your workflow. It provides code optimization, compute unit estimation, code generation, and practical recommendations to help you write safer, faster Zig code and to discover best practices efficiently.

How to use

You interact with the Zig MCP Server through an MCP client. The server exposes a set of tools that you can invoke on Zig source code or natural language prompts. Use optimize_code to improve Zig snippets at a chosen optimization level, estimate_compute_units to gauge memory and time requirements, generate_code to create Zig snippets from descriptions, and get_recommendations to receive actionable guidelines for performance, safety, and style.

How to install

Prerequisites: ensure you have Node.js and npm installed on your machine.

Clone the Zig MCP Server repository and install dependencies.

git clone [repository-url]
cd zig-mcp-server
npm install

Build the server so it can run with the MCP framework.

npm run build

Provide your GitHub token to improve API rate limits and enable server operations that may require authenticated calls. Set the environment variable for your runtime session.

# Create a GitHub token with at least public_repo scope
GITHUB_TOKEN=your_token_here
export GITHUB_TOKEN

Add the Zig MCP Server configuration to your MCP setup so the server can be discovered and started by the MCP runner.

{
  "mcpServers": {
    "zig": {
      "command": "node",
      "args": ["/path/to/zig-mcp-server/build/index.js"],
      "env": {
        "GITHUB_TOKEN": "your_token_here",
        "NODE_OPTIONS": "--experimental-vm-modules"
      },
      "restart": true
    }
  }
}

Additional configuration and notes

The server is configured to run as a local process via Node. The environment variable GITHUB_TOKEN is used to authorize higher API limits, and NODE_OPTIONS enables experimental VM modules for compatible runtime behavior.

Available tools

optimize_code

Analyzes Zig code and applies optimizations at the selected level (Debug, ReleaseSafe, ReleaseFast, ReleaseSmall) to improve performance and size.

estimate_compute_units

Estimates memory usage, time complexity, and allocation patterns to help understand resource requirements.

generate_code

Creates Zig code from natural language descriptions, with support for error handling, testing, performance optimizations, and documentation.

get_recommendations

Provides code improvement recommendations covering style, design patterns, safety, and performance insights.