genpark-personal-agent-tax-deduction-receipt-ocr-categorizer-skill

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Personal Agent Tax Deduction & Freelancer Expense Categorizer. Audits bank transactions and receipts against IRS Schedule C expense categories (home office, software, mileage, meals), calculates tax liability deductions, and isolates audit risk flags.

🌐 Official GenPark AI Ecosystem Links

• GenPark Official Website: https://www.genpark.ai

• LinkedIn Official Page: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Related MCP server: dingdawg-finance-agent

🌟 Key Features

• 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.

• Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.

• Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.

• Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import PersonalAgentTaxDeductionCategorizer

def main():
    agent = PersonalAgentTaxDeductionCategorizer()
    bills = [
        {"merchant": "AWS Cloud", "amount_usd": 184.50, "description": "Compute clusters for development"},
        {"merchant": "Logitech", "amount_usd": 129.99, "description": "MX Master 3S Ergonomic Mouse"},
        {"merchant": "Bistro Deluxe", "amount_usd": 290.00, "description": "Investor dinner"}
    ]
    res = agent.categorize_deductible_expenses(bills, marginal_tax_bracket_pct=32.0)
    print("--- Personal Tax Deduction Analysis ---")
    print(f"Total Spent: ${res['total_spent_usd']} | Total Deductible: ${res['total_deductible_usd']}")
    print(f"Estimated Tax Savings: ${res['estimated_tax_savings_usd']}")
    for exp in res['categorized_expenses']:
        print(f"* [{exp['schedule_c_category']}] {exp['merchant']}: ${exp['deductible_amount_usd']} (Audit Flag: {exp['audit_flag']})")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI. Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550