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Miro MCP
Your AI coding assistant can now read your Miro boards. By implementing the Model Context Protocol (MCP), Miro creates a bridge between your visual thinking canvas and your code editor. This bidirectional link allows AI agents to “see” your architecture diagrams to write better code, and conversely, allows them to draw diagrams on Miro to document your existing codebase.
Key Features
- Context-Aware Coding: Give your AI assistant read access to PRDs, user flows, architecture diagrams, and wireframes stored in Miro to ground its code generation in agreed-upon specs.
- Bidirectional Workflow:
- Visual-to-Code: “Read this user flow on Miro and scaffold the React components for it.”
- Code-to-Visual: “Analyze this repository and draw an entity-relationship diagram on my Miro board.”
- Broad Compatibility: Works natively with Cursor, Claude Code, Windsurf, GitHub Copilot, Replit, and any other MCP-compliant client.
- Simplified Setup: Uses standard OAuth authentication for a secure connection in under 2 minutes, eliminating complex local server configuration.
- Enterprise Security: Inherits Miro’s existing enterprise governance and permission controls.
How It Works
Developers configure their AI tool (e.g., Cursor or Claude Desktop) to connect to the Miro MCP server via OAuth. Once authorized, the AI gains a set of “tools” to interact with Miro.
* Reading: The AI can query specific boards to extract text from sticky notes, interpret relationships between shapes, and understand image captions.
* Writing: The AI can generate new items on the board, creating flowcharts, dropping sticky notes for feedback, or updating status cards based on code progress.
This creates a “Living Spec” where the code and the visual documentation stay in sync automatically.
Use Cases
- Spec-Driven Development: An engineer points Claude at a Product Requirement Document (PRD) on Miro and asks it to write the initial TDD test cases.
- Automated Documentation: A team onboarding a new developer asks their AI to “Map out the authentication flow of this legacy codebase on a new Miro board.”
- Visual Debugging: Asking an AI to visualize a complex race condition by drawing a sequence diagram on Miro based on the logs.
- Design-to-Code: converting low-fidelity wireframes on Miro into frontend boilerplate code.
Pros and Cons
- Pros: Single Source of Truth (aligns code with design); Bidirectional (unlike most tools that only do code-to-doc); Broad Integration (works with almost all major AI editors); Easy Auth (OAuth vs API keys); Free Public Beta.
- Cons: Beta Stability (may have edge cases with complex board items); Context Limits (very large boards might exceed AI context windows); Requires Miro Subscription (for the underlying board access); Dependency on AI Vision/Reasoning (AI might misinterpret messy diagrams).
Pricing
- MCP Server Access: Free during the Public Beta.
- Miro Subscription: Standard Miro plan rates apply (Free, Starter, Business, Enterprise) to create and access boards.
How Does It Compare?
Miro MCP sits at the intersection of “Visual Collaboration” and “AI Coding.”
- Lucidchart / Draw.io: These tools have “Generate Diagram from Text” features, but they lack the deep, two-way integration with AI Code Editors via MCP. They are often “destinations” rather than “tools” available inside your IDE.
- Mermaid.js / PlantUML: These are code-based diagramming tools. They are great for version control but terrible for brainstorming with non-technical PMs. Miro MCP bridges this gap: PMs use Miro, Devs use Code, and the AI translates between them.
- FigJam AI: Similar visual capabilities, but currently lacks a standardized MCP Server implementation for broad compatibility with 3rd-party coding agents like Claude Code or Windsurf.
- Generic “Read URL” MCPs: You could use a generic “Web Reader” MCP to read a public Miro board, but it wouldn’t understand the structure (stickies, connectors) like the official Miro MCP does, nor could it write back to the board.
Final Thoughts
Miro’s adoption of MCP is a strategic masterstroke. It acknowledges that code doesn’t exist in a vacuum—it exists downstream of decisions made on whiteboards. By allowing AI agents to “participate” in the whiteboard session, Miro effectively makes its canvas machine-readable. For teams practicing “Domain-Driven Design” or robust agile planning, this tool removes the friction of manually translating sticky notes into Jira tickets or code comments. It turns the whiteboard from a static artifact into a dynamic part of the codebase.