I'll help you design a comprehensive architecture for building a Raycast extension that connects to an LLM via MCP server through n8n. Here's a detailed breakdown:

Architecture Overview

Your system will have these components:

1. Raycast Extension (React/TypeScript frontend)
2. n8n Workflow (orchestration layer)
3. MCP Server (data/tool provider)
4. LLM Provider (Claude or other)

Raycast Extension Development

Setup & Structure

Key considerations:

• Use Raycast's @raycast/api for UI components and utilities
• Implement proper error handling and loading states
• Cache responses for better UX
• Handle authentication tokens securely

Extension Components

• Command Handler: Main entry point for user queries
• HTTP Client: For communicating with n8n webhook
• Response Parser: Format LLM responses for Raycast UI
• Settings: Allow users to configure API endpoints and tokens

n8n Workflow Design

Workflow Structure

1. Webhook Trigger: Receive POST requests from Raycast
2. MCP Client Node: Connect to your MCP server 1
3. LLM Integration: Send enriched context to Claude/other LLM
4. Response Formatter: Structure response for Raycast
5. HTTP Response: Return formatted data

Key n8n Considerations

• Use n8n's HTTP Request nodes for MCP server communication
• Implement proper error handling and timeouts
• Consider rate limiting and request queuing
• Store API keys securely in n8n credentials

MCP Server Integration

MCP Server Setup

You'll need to deploy an MCP server that exposes tools/data 2. Consider:

• Transport Method: Use HTTP/SSE for n8n compatibility 1
• Tool Definition: Define clear schemas for your tools
• Authentication: Secure your MCP endpoints
• Scaling: Deploy on Vercel for efficient scaling 2

n8n to MCP Communication

LLM Integration Strategy

AI SDK Integration in n8n

While n8n has built-in AI nodes, for more control you might use custom function nodes with the AI SDK 3:

Technical Considerations

Security

• API Key Management: Store sensitive keys in n8n credentials, not in Raycast extension
• Authentication: Implement proper auth between Raycast ↔ n8n ↔ MCP server
• Rate Limiting: Implement both client-side and server-side rate limiting
• Input Validation: Sanitize all inputs before processing

Performance

• Caching: Cache MCP server responses when appropriate
• Streaming: Consider streaming responses for better UX (though Raycast may have limitations)
• Timeout Handling: Set appropriate timeouts for each service call
• Retry Logic: Implement exponential backoff for failed requests

Error Handling

• Graceful Degradation: Handle MCP server or LLM unavailability
• User Feedback: Provide clear error messages in Raycast
• Logging: Comprehensive logging in n8n for debugging
• Fallback Responses: Consider fallback when tools are unavailable

Implementation Steps

1. Start with MCP Server: Deploy a simple MCP server with basic tools 2
2. Build n8n Workflow: Create the orchestration layer
3. Develop Raycast Extension: Build the user interface
4. Test Integration: End-to-end testing of the complete flow
5. Add Advanced Features: Streaming, caching, advanced error handling

Alternative Approaches

Direct MCP Integration

Instead of n8n, you could use the AI SDK's MCP client directly 1, but this would require:

• Running a Node.js backend service
• More complex deployment and scaling considerations
• Direct management of MCP connections

Hybrid Approach

• Use n8n for complex workflows and orchestration
• Direct MCP integration for simple, low-latency queries
• Smart routing based on query complexity

This architecture provides flexibility, scalability, and maintainability while leveraging each tool's strengths. The n8n layer adds powerful workflow capabilities and makes it easier to integrate multiple data sources and tools through your MCP server.

Okay, I understand! You want to focus on getting the Raycast extension built with my help, and you need a clear way to give me instructions. That's a great approach, especially since you're guiding the process.

Building a Raycast extension involves writing React and TypeScript code that runs within the Raycast environment on your Mac. While I can generate this code for you, you won't be able to run or preview the Raycast extension directly within the v0 interface. You'll need to set up a Raycast development environment on your computer to use the code I provide. I can guide you on that part too when we get there.

To help me generate the best possible starting point for your Raycast extension, please use the following prompt template. Fill in the details as best as you can. The more specific you are, the better I can tailor the code to your needs.

Prompt Template for v0 to Build Your Raycast Extension:

"Hi v0, please help me build a Raycast extension. Here are the specifications:

1. Extension & Command Details:

   • Extension Name: (e.g., "My n8n Agent")
   • Command Name (this is what users will type to find it in Raycast): (e.g., "Ask My AI Agent")
   • Command Title (shown at the top of your command's window): (e.g., "Query My n8n Agent")
   • Command Description (a brief explanation of what it does): (e.g., "Sends your question to an n8n workflow and displays the AI's answer.")
   • (Optional) Icon for the command: (You can choose from Raycast's built-in icons later, or suggest one like "speech-bubble")

2. User Input Interface:

   • When the command opens, what should the user see first?

   • I need an input field for the user to type their question.

     • Type of input field: (e.g., TextField for a single line, or TextArea for multiple lines if questions can be long)
     • Label for the input field: (e.g., "Your Question:")
     • Placeholder text (the greyed-out text inside the input field before typing): (e.g., "What would you like to ask the agent?")

3. n8n Webhook Connection:

   • n8n Webhook URL: (Please provide the actual URL. If you're not ready, use a placeholder like https://your-n8n-instance.com/webhook/your-webhook-id)
   • HTTP Method: POST

4. Data to Send to n8n (Request Payload):

   • When the user submits their question, what information should be sent to your n8n webhook?

   • Please describe the JSON structure. For example:

     { "query": "THE_USER_S_QUESTION_WILL_GO_HERE" }

     (Specify the exact key you want to use for the user's input, e.g., "query", "prompt", "userInput")

   • HTTP Headers (if any): (e.g., if your n8n webhook requires an Authorization token. For example: {"Authorization": "Bearer YOUR_N8N_API_KEY"}. For sensitive data like API keys, we should plan to use Raycast's preferences system for secure storage.)

5. Data Expected Back from n8n (Response Payload):

   • What information will n8n send back after processing the request?

   • Please describe the JSON structure. For example:

     { "answer": "THE_AI_S_RESPONSE_WILL_BE_HERE", "confidence_score": 0.95, "source_documents": ["doc1.pdf", "doc2.txt"] }

     (Specify the exact key that holds the main answer you want to display, e.g., "answer", "response", "result")

6. Displaying the Response in Raycast:

   • How should the answer from n8n be shown to the user?

     • Primary Display Method: (e.g., "Show the main 'answer' text in a Raycast <Detail> view, which is good for longer text and supports Markdown formatting.")
     • (Optional) Displaying other information: (e.g., "If 'source_documents' are present, list them below the main answer.")
   • Should there be a loading indicator (e.g., "Thinking...") while waiting for the n8n response? (Yes/No)

7. Basic Error Handling:

   • What should happen if the request to n8n fails (e.g., network error, n8n error)?
   • (e.g., "Show a pop-up error message (a 'Toast' in Raycast terms) like 'Error: Could not reach the AI agent. Please try again.'")

8. Submit Action:

   • What should the button or action be named to send the query? (e.g., "Submit Query", "Ask Agent")
   • Should pressing Enter in the input field also submit the query? (Usually Yes for Raycast forms)

Once you provide these details, I can start generating the TypeScript and React code for your Raycast extension.

Tips for Guiding Me:

• Be Specific, But Don't Worry About Perfection: The more details you give, the closer the first version will be to your vision. However, we can always iterate and refine.
• Placeholders are Fine: If you don't have the final n8n URL or the exact JSON structure for request/response, use placeholders. We can easily update these in the code later.
• Start Simple: We can build a core version first (input -> n8n -> display response) and then add more complex features like conversation history, settings for API keys, etc., in later iterations.
• Think About the User Experience: How do you want it to feel for someone using your Raycast command? Quick? Detailed?

Fill out the template above, and I'll do my best to generate the initial code for your Raycast extension!