Cursor

Setup Cursor to correctly generate Avo code based on your prompt.

Quick use

In chat window type this and Cursor will use Avo's llms.txt file to generate code.

@web https://docs.avohq.io/2.0/llms-full.txt

Permanent setup

1. Press ⌘ CMD+⇧ Shift+P. Or if it's Windows, press ⌃ Ctrl+⇧ Shift+P.
2. Type Add new custom docs
3. Add this: https://docs.avohq.io/2.0/llms-full.txt
4. Now in chat window you can type @docs and choose Avo to provide Avo's docs to Cursor.

Project-level permanent setup

You can setup Avo's llms.txt file to your repo so Cursor can use it by default. (Read more at Cursor docs)

Run this command to save the llms.txt file to .cursor/rules/avohq.mdc

curl -L https://docs.avohq.io/2.0/llms-full.txt --create-dirs -o .cursor/rules/avo.mdc

MCP server

MCP is a an API to communicate with AI models. You can add MCP servers and Cursor will communicate with them to get more accurate results.

I suggest using Context7 MCP server which provides many libraries including Avo's docs.

1. Go to Cursor settings ⌘ CMD+⇧ Shift+J (or ⌃ Ctrl+⇧ Shift+J on Windows)

2. Click MCP from the left sidebar

3. Click Add new global MCP server

4. Add this:

// .cursor/mcp.json
{
  "mcpServers": {
   "Context7": {
     "type": "stdio",
     "command": "npx",
     "args": ["-y", "@upstash/context7-mcp@latest"]
   }
  }
}

5. Now in Agent Mode you can ask AI anything about Avo, and write use context7 at the end of your prompt.

For example:

create a new Avo resource for a product model. use context7