Zig MCP Server

A Model Context Protocol (MCP) server that provides Zig language tooling, code analysis, and documentation access. This server enhances AI capabilities with Zig-specific functionality including code optimization, compute unit estimation, code generation, and best practices recommendations.

Features

Tools

1. Code Optimization (optimize_code)

Analyzes and optimizes Zig code with support for different optimization levels:

• Debug
• ReleaseSafe
• ReleaseFast
• ReleaseSmall

// Example usage
{
  "code": "const std = @import(\"std\");\n...",
  "optimizationLevel": "ReleaseFast"
}

2. Compute Units Estimation (estimate_compute_units)

Estimates computational complexity and resource usage of Zig code:

• Memory usage analysis
• Time complexity estimation
• Allocation patterns detection

// Example usage
{
  "code": "const std = @import(\"std\");\n..."
}

3. Code Generation (generate_code)

Generates Zig code from natural language descriptions with support for:

• Error handling
• Testing
• Performance optimizations
• Documentation

// Example usage
{
  "prompt": "Create a function that sorts an array of integers",
  "context": "Should handle empty arrays and use comptime when possible"
}

4. Code Recommendations (get_recommendations)

Provides code improvement recommendations and best practices:

• Style and conventions
• Design patterns
• Safety considerations
• Performance insights

// Example usage
{
  "code": "const std = @import(\"std\");\n...",
  "prompt": "Improve performance and safety"
}

Resources

1. Language Reference (zig://docs/language-reference)

   • Official Zig language documentation
   • Syntax and features guide
   • Best practices

2. Standard Library Documentation (zig://docs/std-lib)

   • Complete std library reference

   • Function signatures and usage

   • Examples and notes

            3. **Popular Repositories** (`zig://repos/popular`)
     

   • Top Zig projects on GitHub

   • Community examples and patterns

   • Real-world implementations

Installation

1. Clone the repository:

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

2. Install dependencies:

npm install

3. Build the server:

npm run build

4. Configure environment variables:

# Create a GitHub token for better API rate limits
# https://github.com/settings/tokens
# Required scope: public_repo
GITHUB_TOKEN=your_token_here

5. Add to MCP settings:

{
  "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
    }
  }
}

Usage Examples

1. Optimize Code

const result = await useMcpTool("zig", "optimize_code", {
  code: `
    pub fn fibonacci(n: u64) u64 {
        if (n  arr[j + 1]) {
                    const temp = arr[j];
                    arr[j] = arr[j + 1];
                    arr[j + 1] = temp;
                }
            }
        }
    }
  `
});

3. Generate Code

const result = await useMcpTool("zig", "generate_code", {
  prompt: "Create a thread-safe counter struct",
  context: "Should use atomic operations and handle overflow"
});

4. Get Recommendations

            
        
            
                const result = await useMcpTool("zig", "get_recommendations", {
  code: `
    pub fn main() !void {
        var list = std.ArrayList(u8).init(allocator);
        var i: u32 = 0;
        while (true) {
            if (i >= 100) break;
            try list.append(@intCast(u8, i));
            i += 1;
        }
    }
  `,
  prompt: "performance"
});

Development

Project Structure

zig-mcp-server/
├── src/
│   └── index.ts    # Main server implementation
├── build/          # Compiled JavaScript
├── package.json    # Dependencies and scripts
└── tsconfig.json   # TypeScript configuration

Building

# Development build with watch mode
npm run watch

# Production build
npm run build

Testing

npm test

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - see the LICENSE file for details.