Personal Agent Tax Deduction & Freelancer Expense Categorizer
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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: genpark-personal-agent-subscription-stealth-price-hike-detector-skill
🌟 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, andtools/call.Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
Dual Deployment Ecosystem: Verified across
alphaparkincandAlpha-Parkorganizations 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.pyExecute 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
Related MCP Connectors
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Open-source AI accounting skills verified by licensed accountants (tax, VAT, payroll).
Turn bank statement PDFs into categorized, balance-checked transactions and reports.
Proof-of-spend and reserve checks for AI agents: receipts, spend audit, NAV, reconciliation.
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