How to Integrate Lovable with Miro

Before building the Edge Function integration for your deployed app, set up the Miro MCP personal connector in Lovable to give the AI context about your boards during development. This is a separate, simpler setup that enhances Lovable's AI understanding — it does not appear in the deployed app, only in the builder. In Lovable, open Settings by clicking the gear icon in the top-right corner. Navigate to Connectors → Personal connectors. Find Miro in the list of nine available personal connectors and click Connect. Follow the OAuth authorization flow to grant Lovable read access to your Miro boards. Once connected, you can use @Miro in your chat prompts to reference specific boards: for example, 'Build a task management UI based on the user story map in @Miro board ID abc123'. With the MCP connector active, Lovable's AI can read your board content during the building process, generating components that match your actual wireframes and diagrams. This is distinct from the Edge Function integration you will build next — the MCP connector is for the builder, the Edge Function is for the deployed application.

To access the Miro REST API v2 from your deployed Lovable app, you need OAuth2 credentials from a Miro developer application. Navigate to miro.com/app/settings/user-profile/apps in your browser while logged into Miro. Click 'Create new app'. Give your app a name (such as 'Lovable Runtime Integration') and optionally a description. Select the team workspace this app will access. After creation, Miro shows your Client ID and Client Secret — copy both immediately. For a personal single-user integration where you are accessing your own boards, Miro offers a way to generate access tokens for development. In the app settings, look for the 'OAuth & Permissions' section and add the required scopes: boards:read (to list boards and fetch items) and boards:write (if you want to create or update items). Then use Miro's OAuth flow to generate an access token for your account, or generate a personal developer token from the app's testing tools. The Miro REST API v2 base URL is https://api.miro.com/v2. All requests use Bearer token authentication in the Authorization header.

Add your Miro access token to Cloud Secrets. Open the Cloud tab in Lovable by clicking the '+' icon next to the Preview panel. Go to Secrets. Click 'Add new secret', name it MIRO_ACCESS_TOKEN, paste your token, and click Save. This token is the Bearer credential your Edge Function will use for all Miro API calls. Now ask Lovable to create the Edge Function. The Miro REST API v2 provides clean, well-documented JSON endpoints. The key endpoints for extracting whiteboard content are: GET /boards (list boards), GET /boards/{boardId}/items (list all items), and GET /boards/{boardId}/frames (list frames/groups). You can also filter items by type using the type query parameter. The Edge Function exposes these as named actions the frontend can call.

1// supabase/functions/miro-proxy/index.ts
2import { serve } from 'https://deno.land/std@0.168.0/http/server.ts';
3
4const corsHeaders = {
5  'Access-Control-Allow-Origin': '*',
6  'Access-Control-Allow-Headers': 'authorization, x-client-info, apikey, content-type',
7};
8
9serve(async (req) => {
10  if (req.method === 'OPTIONS') {
11    return new Response('ok', { headers: corsHeaders });
12  }
13
14  try {
15    const token = Deno.env.get('MIRO_ACCESS_TOKEN');
16    if (!token) {
17      return new Response(
18        JSON.stringify({ error: 'MIRO_ACCESS_TOKEN not configured' }),
19        { status: 500, headers: { ...corsHeaders, 'Content-Type': 'application/json' } }
20      );
21    }
22
23    const { action, payload = {} } = await req.json();
24    const base = 'https://api.miro.com/v2';
25    const headers = { Authorization: `Bearer ${token}`, Accept: 'application/json' };
26
27    switch (action) {
28      case 'list_boards': {
29        const params = new URLSearchParams({ limit: '50' });
30        if (payload.query) params.set('query', payload.query);
31        const res = await fetch(`${base}/boards?${params}`, { headers });
32        const data = await res.json();
33        return new Response(JSON.stringify(data), {
34          headers: { ...corsHeaders, 'Content-Type': 'application/json' },
35        });
36      }
37      case 'get_board': {
38        const res = await fetch(`${base}/boards/${payload.board_id}`, { headers });
39        const data = await res.json();
40        return new Response(JSON.stringify(data), {
41          headers: { ...corsHeaders, 'Content-Type': 'application/json' },
42        });
43      }
44      case 'get_board_items': {
45        const allItems: unknown[] = [];
46        let cursor = '';
47        do {
48          const params = new URLSearchParams({ limit: '50' });
49          if (payload.item_type) params.set('type', payload.item_type);
50          if (cursor) params.set('cursor', cursor);
51          const res = await fetch(`${base}/boards/${payload.board_id}/items?${params}`, { headers });
52          const data = await res.json();
53          allItems.push(...(data.data || []));
54          cursor = data.cursor || '';
55        } while (cursor);
56        return new Response(JSON.stringify({ items: allItems, total: allItems.length }), {
57          headers: { ...corsHeaders, 'Content-Type': 'application/json' },
58        });
59      }
60      case 'get_frames': {
61        const res = await fetch(`${base}/boards/${payload.board_id}/frames`, { headers });
62        const data = await res.json();
63        return new Response(JSON.stringify(data), {
64          headers: { ...corsHeaders, 'Content-Type': 'application/json' },
65        });
66      }
67      default:
68        return new Response(
69          JSON.stringify({ error: `Unknown action: ${action}` }),
70          { status: 400, headers: { ...corsHeaders, 'Content-Type': 'application/json' } }
71        );
72    }
73  } catch (err) {
74    return new Response(
75      JSON.stringify({ error: err.message }),
76      { status: 500, headers: { ...corsHeaders, 'Content-Type': 'application/json' } }
77    );
78  }
79});

With the Edge Function ready, ask Lovable to build the core interface for your Miro integration use case. The most impactful pattern for product teams is extracting sticky note content from boards and making it actionable in a structured app — turning brainstorming outputs into tracked items, sprint retro findings into action items, or user story maps into prioritized backlogs. The extraction interface needs three components: a board selector (loaded from list_boards), a content preview showing what will be extracted (loaded from get_board_items), and an action panel for structuring and saving the extracted data to Supabase. The board's frame structure (from get_frames) provides the grouping context — sticky notes within a frame belong to the same theme or epic. For complex whiteboard-to-app workflow integrations — including bidirectional sync where app updates flow back to Miro, or AI-powered categorization of extracted sticky note content — RapidDev's team can help architect the full data pipeline.