---

name: firefly-iii-mcp-connector description: "MCP connector for Firefly III personal finance – enables AI-powered transaction management, budgeting, and financial reporting through Claude" license: AGPL tags: ["business","finance","mcp","firefly","budgeting"] difficulty: beginner time_to_master: "4-8 weeks" version: "1.0.0"

Firefly Iii MCP Connector

Overview

This skill enables Claude to interact with Firefly Iii through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Firefly Iii's REST API, allowing natural language control of Firefly Iii operations, intelligent automation, and AI-powered assistance for Firefly Iii workflows.

When to Use This Skill

• Transaction categorization and tagging via AI
• Budget creation and monitoring
• Bill tracking and payment scheduling
• Financial goal tracking and projections
• Import/export and reconciliation

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Firefly Iii      │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (REST API      )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Firefly Iii operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.

Key Endpoints/Interfaces

/api/v1/transactions, /api/v1/budgets, /api/v1/accounts, /api/v1/bills, /api/v1/categories

Implementation

// Firefly Iii MCP Server 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: "firefly-iii-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Firefly Iii 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 }) => {
    // Call Firefly Iii REST API
    const response = await fetch(`${BASE_URL}/api/v1/transactions`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Firefly Iii",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}/api/v1/transactions`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Firefly Iii data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}/api/v1/transactions`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "firefly-iii-mcp-connector": {
      "command": "node",
      "args": ["path/to/firefly-iii-mcp-connector/index.js"],
      "env": {
        "FIREFLY_III_API_KEY": "your-api-key",
        "FIREFLY_III_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Firefly Iii API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Firefly Iii API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Categorize all uncategorized transactions from last month and update the monthly budget report"

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

• Firefly Iii Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

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.