| name | planka-mcp-connector |
| description | MCP connector for Planka project tracking – enables AI-powered Kanban board management, task tracking, and team coordination through Claude |
| license | AGPL |
| tags | ["product","kanban","mcp","planka"] |
| difficulty | beginner |
| time_to_master | 4-8 weeks |
| version | 1.0.0 |
Planka MCP Connector
Overview
This skill enables Claude to interact with Planka through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Planka's REST API, allowing natural language control of Planka operations, intelligent automation, and AI-powered assistance for Planka workflows.
When to Use This Skill
- Project and board setup via natural language
- Card management with labels and due dates
- Task assignment and notification management
- Board analytics and progress reporting
- Attachment and comment management
Architecture
┌─────────────┐ ┌─────────────────┐ ┌──────────────────┐
│ Claude │────▶│ MCP Server │────▶│ Planka │
│ (Client) │◀────│ (TypeScript) │◀────│ (REST API )│
└─────────────┘ └─────────────────┘ └──────────────────┘
Core Concepts
MCP Server Setup
The connector implements an MCP server that exposes Planka operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.
Key Endpoints/Interfaces
/api/projects, /api/boards, /api/lists, /api/cards, /api/tasks
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: "planka-mcp-connector",
version: "1.0.0",
});
server.tool(
"list_resources",
"List and query Planka 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}/api/projects`, {
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": {
"planka-mcp-connector": {
"command": "node",
"args": ["path/to/planka-mcp-connector/index.js"],
"env": {
"PLANKA_API_KEY": "your-api-key",
"PLANKA_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 Planka 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 Planka API; sanitize outputs
- Logging: Log all API interactions for debugging and audit purposes
Example Prompts
"Set up a new project board with lists for Backlog, In Progress, Review, and Done"
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 |
Part of SkillGalaxy - 10,000+ comprehensive skills for AI-assisted development.