genpark-financial-audit

io.github.Alpha-ParkPublished by GitHub account Alpha-Parkv1.0.1Updated Oct 24, 2026

Arithmetic consistency checks for supplied financial statement data.

The publisher's own server: audit.domains - Domain Valuation

VerifiedSTDIODesktop onlyFinanceData & Analytics

Overview

AI-generated overview

Local MCP server that runs arithmetic consistency checks on financial statement figures you supply, such as balance sheets and income statements.

What you can do
  • Audit a supplied balance sheet for arithmetic consistency
  • Audit a supplied income statement against a simplified model
  • Check cross-footing of supplied figures
  • Run a benchmark audit example on an isolated instance
When to use it
Useful when you already have financial statement numbers in hand and want an assistant to check that totals, subtotals and cross-footings add up within a tolerance. It is not for extracting figures from PDFs, verifying source authenticity, assessing accounting compliance, or forming an audit opinion.
Requirements
Runs as a local stdio process and needs Python 3.9 or newer; the library and server have no runtime dependencies. Install from the GitHub release wheel (PyPI publication is still pending), then point the MCP client at the installed command or python -m genpark_financial_audit. A .mcpb desktop bundle is also offered and needs a Python 3.9+ interpreter on PATH. No authentication, environment variables or headers are declared.
Before you install
Monetary values must use consistent units, and the tolerance defaults to 0.5 of those units, so mixed units can produce misleading results. The checks cover arithmetic only and do not validate source data or accounting rules. Each MCP process keeps its own state. The package is distributed from GitHub releases rather than PyPI, so confirm the source before installing.

Installation

In SourceWeft

  1. Open genpark-financial-audit in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

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.

sh
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

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

MCP stdio configuration

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

json
{  "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

sh
python -m unittest discover -s testspython -m pip install mcppython tests/check_mcp.pypython -m pip install build twinepython -m buildpython -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.

Source: README.md at commit 65ecf67

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v1.0.1LatestSep 27, 2026

Score

40% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

    Declare a license in the repository or the registry entry.

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