To make Rust development even better, we’ve built the Shuttle MCP server.

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What’s this for?

The Shuttle MCP server supercharges your development workflow in two powerful ways: Contextual Knowledge: It acts as a bridge between Shuttle’s comprehensive documentation and your AI coding assistant, providing instant access to Shuttle-specific guidance without leaving your editor. No more tab-switching between docs and code! Powerful Development Tools: Beyond just documentation, the MCP server gives your AI agent direct access to Shuttle’s tooling ecosystem. Your assistant can help you deploy services, fetch logs, and interact with the Shuttle console - all through natural conversation. This makes development with Rust and Shuttle significantly faster and more intuitive.

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Installation

The Shuttle MCP server comes with the Shuttle CLI (version 0.56 and later), make sure you have the Shuttle CLI installed first.

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Configuration

Choose your preferred AI coding environment and add the Shuttle MCP server configuration. 
{
  "mcpServers": {
    "Shuttle": {
      "command": "shuttle",
      "args": ["mcp", "start"]
    }
  }
}

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Configuration File Locations

IDEConfiguration Path
Cursor~/.cursor/mcp.json (global) or .cursor/mcp.json (project)
Windsurf~/.codeium/windsurf/mcp_config.json
VSCodeUser settings or .vscode/mcp.json (project)
Claude DesktopmacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

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You’re all set! 🚀

Your AI coding assistant now has direct access to Shuttle’s knowledge base and tooling. Let’s see it in action!

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How to use?

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Prompting Best Practices

To get the most out of the Shuttle MCP server, end your prompts with Use the Shuttle MCP server, this makes sure that the AI agent always references the most up-to-date Shuttle documentation and examples when helping you with your code. It also makes the AI agent more likely to use the Shuttle MCP server for deployments, logs, project, status, etc. 
Build a Simple Todo App with Axum and then deploy it to Shuttle.

Use the Shuttle MCP server.
Create AI agent rules in your project to establish consistent coding practices. Choose the appropriate file based on your AI coding assistant:
  • Cursor: .cursor/rules/shuttle.mdc
  • Windsurf: .windsurf/rules/shuttle.md
  • Claude Code: CLAUDE.md
# Shuttle Rules

- Always use the Shuttle MCP to get the latest docs and examples
- Whenever needed, use the Shuttle MCP server for deployments, logs, project, status, etc.

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For Cursor Users: Enable “Always Apply”

When adding rules in Cursor, make sure to select “Always Apply” to ensure the rule is applied to all AI chats automatically. Cursor Always Apply setting

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MCP Server in Action

Let’s see the MCP server in action! We’ll use Cursor and ask the AI agent to build a simple full stack todo app with Axum and deploy it to Shuttle. Here is the prompt we’ll use, and we’ll leave the rest to the AI agent:
Prompt
Build a Simple Full Stack Todo App with a beautiful UI with Axum and then deploy it to Shuttle.

Use the Shuttle MCP server.

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AI Agent Accessing Latest Documentation

The AI agent immediately starts by searching through Shuttle’s latest documentation using the MCP server. This ensures it has access to the most up-to-date information about Shuttle’s features, best practices, and examples before beginning development. AI agent searching Shuttle documentation via MCP server As you can see, the AI agent proactively searches for relevant documentation sections, ensuring it builds the todo app using current Shuttle patterns and recommendations.

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Debugging with MCP Tools

The power of the MCP server really shines when debugging. In this example, the AI agent deployed the app to the cloud and checked the status - everything looked good. But when fetching the logs, it encountered a runtime panic. AI agent debugging with MCP tools The AI agent immediately identified the issue and fixed the error, then deployed again. This showcases the power of having MCP tools integrated directly into your AI assistant’s workflow.

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Success on Second Try

After fixing the runtime error, the AI agent successfully deployed the application on the second attempt: Successful second deployment

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The Final Result

Here’s the beautiful todo app UI that was deployed to the cloud: Deployed todo app UI The entire process - from initial development to debugging and successful deployment - was handled seamlessly by the AI agent using the Shuttle MCP server tools. This demonstrates how the MCP integration makes Shuttle development faster, more intuitive, and more reliable. We can’t wait to see what amazing projects you create with the Shuttle MCP server, and we’d love to hear your feedback!