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Genpark Complex Financial Formula Audit Validator Skill MCP Server

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Arithmetic consistency checks for supplied financial statement data.

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Arithmetic consistency checks for supplied financial statement data.

Security Report

0.0
Use Caution0.0Moderate Risk

4 tools verified · Open access · No issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Remote servers are capped at 8.0 because source code is not available for review. The score reflects endpoint verification only.

Documentation

View on GitHub

From the project's GitHub README.

genpark-financial-audit

Arithmetic consistency checks for supplied financial statement data.

This checks supplied numbers and a simplified income-statement model. It does not extract PDFs, verify source authenticity, check accounting compliance, or provide an audit opinion. Monetary values must use consistent units; tolerance defaults to 0.5 of those units.

Install from the GitHub release

Python 3.9 or newer. The library and stdio MCP server have no runtime dependencies.

python -m pip install https://github.com/Alpha-Park/genpark-complex-financial-formula-audit-validator-skill/releases/download/v1.0.1/genpark_financial_audit-1.0.1-py3-none-any.whl

PyPI publication is pending account setup. The intended PyPI project is genpark-financial-audit; do not assume pip install genpark-financial-audit is available until the project is published.

Python usage

from genpark_financial_audit import FinancialFormulaAuditValidator
client = FinancialFormulaAuditValidator()
print(client.run_benchmark_financial_audit())

MCP stdio configuration

After installing the wheel, configure your MCP client with the installed command:

{
  "mcpServers": {
    "genpark-financial-audit": {
      "command": "genpark-financial-audit",
      "args": []
    }
  }
}

If the command is not on PATH, use its absolute path or python -m genpark_financial_audit with the same interpreter where you installed the wheel. The GitHub release also contains a .mcpb bundle for clients supporting desktop extensions. That bundle requires a Python 3.9+ interpreter on PATH; it bundles the server source.

Available tools: audit_balance_sheet, audit_income_statement, audit_cross_footing, run_benchmark_financial_audit. tools/list returns required arguments and JSON schemas. Each MCP process holds its own state. Benchmark tools use isolated instances.

Development

python -m unittest discover -s tests
python -m pip install mcp
python tests/check_mcp.py
python -m pip install build twine
python -m build
python -m twine check dist/*

python mcp_server.py --test runs the deterministic example; it is not a protocol conformance test. The MCP client check exercises initialize, tools/list, tools/call and ping over stdio.

Distribution

GitHub source and release artifacts are the primary distribution until PyPI is configured. Registry submissions are tracked separately; a manifest is not proof of registry acceptance. See PUBLISHING.md for the repeatable PyPI workflow.

MIT license. Maintained by GenPark.

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