| name | focalboard-mcp-connector |
| description | MCP connector for Focalboard/Mattermost Boards – enables AI-driven project tracking, view management, and team collaboration through Claude |
| license | AGPL |
| tags | ["product","project-management","mcp","focalboard","mattermost"] |
| difficulty | intermediate |
| time_to_master | 4-8 weeks |
| version | 1.0.0 |
Focalboard MCP Connector
Overview
This skill enables Claude to interact with Focalboard through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Focalboard's REST API, allowing natural language control of Focalboard operations, intelligent automation, and AI-powered assistance for Focalboard workflows.
When to Use This Skill
- Board and view creation from templates via AI
- Card property management and filtering
- Calendar and gallery view generation
- Template creation from existing workflows
- Cross-board reporting and analytics
Architecture
┌─────────────┐ ┌─────────────────┐ ┌──────────────────┐
│ Claude │────▶│ MCP Server │────▶│ Focalboard │
│ (Client) │◀────│ (TypeScript) │◀────│ (REST API )│
└─────────────┘ └─────────────────┘ └──────────────────┘
Core Concepts
MCP Server Setup
The connector implements an MCP server that exposes Focalboard operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.
Key Endpoints/Interfaces
/api/v2/boards, /api/v2/boards/:id/blocks, /api/v2/teams/:id/boards, /api/v2/boards/:id/members
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: "focalboard-mcp-connector",
version: "1.0.0",
});
server.tool(
"list_resources",
"List and query Focalboard 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/v2/boards`, {
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": {
"focalboard-mcp-connector": {
"command": "node",
"args": ["path/to/focalboard-mcp-connector/index.js"],
"env": {
"FOCALBOARD_API_KEY": "your-api-key",
"FOCALBOARD_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 Focalboard 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 Focalboard API; sanitize outputs
- Logging: Log all API interactions for debugging and audit purposes
Example Prompts
"Create a product roadmap board with quarterly views and priority labels"
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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