| name | krita-mcp-connector |
| description | MCP connector for Krita digital painting – enables AI-assisted brush management, canvas operations, and painting workflow automation through Claude |
| license | GPL |
| tags | ["creative","digital-painting","mcp","krita"] |
| difficulty | advanced |
| time_to_master | 4-8 weeks |
| version | 1.0.0 |
Krita MCP Connector
Overview
This skill enables Claude to interact with Krita through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Krita's Python Plugin API, allowing natural language control of Krita operations, intelligent automation, and AI-powered assistance for Krita workflows.
When to Use This Skill
- Brush preset creation and management
- Layer and mask automation
- Color palette generation from references
- Animation timeline management
- Batch export and format conversion
Architecture
┌─────────────┐ ┌─────────────────┐ ┌──────────────────┐
│ Claude │────▶│ MCP Server │────▶│ Krita │
│ (Client) │◀────│ (Python/TypeScript) │◀────│ (Python Plugin )│
└─────────────┘ └─────────────────┘ └──────────────────┘
Core Concepts
MCP Server Setup
The connector implements an MCP server that exposes Krita operations as tools Claude can invoke. The server translates natural language intentions into Python Plugin API calls.
Key Endpoints/Interfaces
Krita.instance(), Document.createNode(), Node.setPixelData(), Krita.action(), Document.exportImage()
Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
const server = new McpServer({
name: "krita-mcp-connector",
version: "1.0.0",
});
server.tool(
"list_resources",
"List and query Krita resources with optional filters",
{
query: z.string().optional().describe("Search query or filter"),
limit: z.number().optional().describe("Max results to return"),
},
async ({ query, limit }) => {
const response = await fetch(`${BASE_URL}Krita.instance()`, {
headers: { "Authorization": `Bearer ${API_KEY}` },
});
const data = await response.json();
return {
: [{ : , : .(data, , ) }],
};
}
);
server.(
,
,
{
: z.().(),
: z.({}).().().(),
},
({ name, config }) => {
response = (, {
: ,
: {
: ,
: ,
},
: .({ name, ...config }),
});
data = response.();
{
: [{ : , : }],
};
}
);
server.(
,
,
{
: z.().(),
: z.().().(),
},
({ , timeframe }) => {
response = (, {
: { : },
});
data = response.();
{
: [{ : , : .(data, , ) }],
};
}
);
transport = ();
server.(transport);
Claude Desktop Configuration
{
"mcpServers": {
"krita-mcp-connector": {
"command": "node",
"args": ["path/to/krita-mcp-connector/index.js"],
"env": {
"KRITA_API_KEY": "your-api-key",
"KRITA_BASE_URL": "https://your-instance-url"
}
}
}
}
Best Practices
- Authentication: Store API keys securely using environment variables; never hardcode credentials
- Rate Limiting: Implement request throttling to respect Krita API rate limits
- Error Handling: Provide clear, actionable error messages for common failure scenarios
- Pagination: Handle paginated responses for large datasets efficiently
- Caching: Cache frequently accessed read-only data to reduce API calls
- Security: Validate all inputs before passing to the Krita API; sanitize outputs
- Logging: Log all API interactions for debugging and audit purposes
Example Prompts
"Create a new canvas with a 3-layer setup for sketch, ink, and color with appropriate blend modes"
Security Considerations
- All API credentials must be stored as environment variables
- Implement input validation and sanitization for all tool parameters
- Use HTTPS for all API communications
- Follow the principle of least privilege for API token permissions
- Audit log all write operations for compliance tracking
Resources
Changelog
| Version | Date | Changes |
|---|
| 1.0.0 | 2026-04-01 | Initial MCP connector skill |
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