Kroki-MCP MCP Server: Enhanced Diagram Conversion and Integration

Overview: What is Kroki-MCP MCP Server?

Kroki-MCP is a sophisticated command-line tool that offers advanced diagram conversion capabilities, seamlessly integrated into Model Context Protocol (MCP). This server supports text-based diagrams generated by tools like PlantUML and Mermaid and converts them into various image formats using a Kroki backend. With customizable configurations and versatile MCP support, Kroki-MCP facilitates robust AI application integration, making it an indispensable tool for developers working in the realm of Model Context Protocol.

Kroki-MCP server operates as both a command-line interface and an MCP tool, ensuring flexibility and interoperability across different workflows. Its modular design allows users to extend functionality by adding support for additional diagram types and output formats. By leveraging this powerful tool, AI applications such as Claude Desktop, Continue, and Cursor can streamline their development and deployment processes.

🔧 Core Features & MCP Capabilities

Kroki-MCP MCP Server introduces several core features that enhance its utility within the Model Context Protocol ecosystem:

1. MCP Integration: Kroki-MCP exposes diagram conversion functionalities through an MCP tool interface—specifically, it leverages github.com/mark3labs/mcp-go, enabling seamless communication between AI applications and external tools.

2. Flexible Modes of Operation: Users can choose from two modes: Server-Sent Events (SSE) or STDIO. SSE mode streamlines real-time diagram processing, while STDIO provides a command-line interface for batch conversions, making it versatile across various scenarios.

3. Customizable Backend Hosts and Formats: With support for png, svg, jpeg, and pdf formats, users can output their diagrams in the desired quality and style. The server is also configurable to use any Kroki backend host, ensuring flexibility and reliability regardless of network conditions or local setup preferences.

4. Modular Extensibility: Kroki-MCP includes comprehensive client logic for interacting with the Kroki backend and flexible configuration management for integrating more diagram types. This modular architecture allows developers to extend the tool's capabilities easily, adapting it to evolving requirements as they arise.

5. Real-time Data Streaming (SSE mode): By streaming results directly through SSE, users achieve low-latency processing of diagrams, optimizing performance in real-world applications where quick feedback loops are essential.

6. Integrated Debugging and Logging: The logging feature allows for detailed tracking and diagnosis, making it easier to troubleshoot issues and enhance the user experience when deploying Kroki-MCP as part of larger AI workflows.

⚙️ MCP Architecture & Protocol Implementation

The architecture of Kroki-MCP is meticulously designed to ensure robustness and interoperability within the Model Context Protocol framework. By adhering to standardized APIs and communication protocols, this server can integrate seamlessly with various MCP clients, enhancing their capabilities for data processing and diagramming tasks.

Diagram: MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[Kroki-MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

In this flow, an AI application leverages the MCP client to communicate with the Kroki-MCP server. The protocol facilitates real-time data streaming and batch operations for diagram conversions. From there, the Kroki backend processes these requests and sends the results back through the same channel.

Diagram: Data Architecture

graph TD;
    subgraph "Diagram Conversion Workflow"
        A[Input Diagram Code] --> B[Parse Diagram]
        B --> C[Convert to Image]
        C --> D[Output Image]
    end

    E["MCP Client"] --> F[Kroki-MCP Server];
    F --> G[Process and Send Response]
    G --> H[AI Application]
end

In this data architecture, the diagram input is parsed using tools like PlantUML or Mermaid. The diagram code is then converted to an image format using the Kroki backend, and finally, the resulting image is outputted. Throughout this process, the MCP client acts as a liaison, ensuring seamless communication between AI applications and external tools.

🚀 Getting Started with Installation

Running as a Command-line Tool:

# Default (SSE mode, PNG, default Kroki host)
kroki-mcp

# Specify output format
kroki-mcp --format svg

# Use STDIO mode
kroki-mcp --mode stdio --format pdf

# Specify a custom Kroki server
kroki-mcp --kroki-host http://localhost:8000

Running as an MCP Server:

go run --list github.com/utain/kroki-mcp/cmd/kroki-mcp@latest --mode sse --format png --kroki-host https://kroki.io

For detailed flags and options, execute the command with --help.

💡 Key Use Cases in AI Workflows

Kroki-MCP serves multiple critical roles within AI workflows:

1. Real-time Documentation Generation: During software development, real-time documentation is essential for maintaining up-to-date project statuses and specifications. With Kroki-MCP's low-latency SSE capabilities, developers can quickly visualize changes via diagrams.

2. AI Model Training and Evaluation: In the context of machine learning, visualizing model architectures and training dynamics is crucial. Kroki-MCP seamlessly converts textual descriptions into clear illustrations, supporting the iterative development process and facilitating clearer communication among team members.

🔌 Integration with MCP Clients

Kroki-MCP's compatibility matrix highlights its extensive support for popular AI applications:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✕
Continue	✅	✕	✕
Cursor	❌	✕	✕

This matrix indicates that Kroki-MCP is fully compatible with tools and resources in the AI domain, but lacks support for certain prompt functionalities. These integration points are critical for ensuring a seamless experience when using Kroki-MCP within an MCP-enabled workflow.

📊 Performance & Compatibility Matrix

Kroki-MCP is designed to perform efficiently across different environments and systems:

• Environment Support: Works seamlessly on both Windows and Linux platforms.
• Performance Metrics: Capable of processing diagrams quickly, with results typically returned in sub-second times for smaller images.
• Compatibility Overview: Robust support for major diagram types and image formats, ensuring broad applicability.

Platform	Supported Formats	Prompt Support
Windows	png, svg	❌
Linux	png, svg, jpeg	✕

This compatibility matrix reflects the wide-range of environments where Kroki-MCP can operate effectively. Developers can leverage these details to ensure that their AI workflows are robust and performant.

🛠️ Advanced Configuration & Security

Kroki-MCP comes equipped with configurable options that enhance its utility:

Example Configuration Code

{
  "mcpServers": {
    "kroki-mcp": {
      "command": "go",
      "args": [
        "run",
        "github.com/utain/kroki-mcp/cmd/kroki-mcp@latest",
        "-m", "stdio",
        "-f", "png",
        "--kroki-host", "https://kroki.io"
      ]
    }
  }
}

Users can modify the configuration to suit their specific needs, such as changing output formats or specifying different backend hosts.

Security Considerations

• Host Verification: Ensure that only trusted Kroki backend servers are utilized to prevent unauthorized access.
• Environment Variables: Use environment variables for sensitive data like API keys and passwords.
• Logging Levels: Adjust logging levels based on deployment context, enabling detailed tracing during development and more minimalistic output in production.

❓ Frequently Asked Questions (FAQ)

1. Q: Can Kroki-MCP support additional diagram types?

   • A: Yes, Kroki-MCP is extensible through its modular architecture. You can add support for additional diagram types by modifying the relevant source code and recompiling the server.

2. Q: How does Kroki-MCP handle large diagrams?

   • A: Kroki-MCP processes diagrams efficiently using a combination of image formats and backend capabilities, ensuring that even complex diagrams are handled effectively without compromising performance.

3. Q: Is there any difference between SSE and STDIO modes in terms of security?

   • A: Both modes have their own security considerations. SSE mode provides real-time updates but may expose additional network surfaces. STDIO mode is more suited for batch processing and local deployments, offering a simpler security profile.

4. Q: Can I run Kroki-MCP on different operating systems?

   • A: Currently, Kroki-MCP supports both Windows and Linux platforms. However, support for other operating systems can be added through contributions that extend the existing codebase.

5. Q: What are the implications of using a custom Kroki server host?

   • A: Using a custom host allows greater flexibility in deployment scenarios but requires careful configuration to ensure compatibility and security. Custom hosts must comply with Kroki's API documentation and security guidelines.

Conclusion

Kroki-MCP stands out as a powerful, flexible, and robust solution for diagram conversion within the Model Context Protocol framework. Its ability to integrate seamlessly with leading AI applications and its comprehensive feature set make it an invaluable tool for developers looking to enhance their workflow efficiency. As Kroki-MCP continues to evolve, it promises to serve as a cornerstone of innovative AI development practices.

By positioning itself as a cutting-edge solution backed by strong community support and ongoing evolution, Kroki-MCP is poised to play a pivotal role in shaping the future of AI and software development workflows.