Finvariant Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Finvariant Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
<!-- mcp-name: io.github.arikanatakan/finvariant-mcp -->
An MCP server that exposes finvariant, the deterministic financial-statement integrity checker for Python, as a tool for AI agents: hand it income statement, balance sheet and cash flow data and it verifies that the balance sheet balances, the cash flow ties to the balance sheet, subtotals foot, and the three statements articulate.
Agents asked to build, read or summarise financial statements tend to produce the numbers themselves: a balance sheet that does not balance, a cash flow that does not tie, a retained-earnings figure that does not roll forward. Generated financial statements fail silently. The check belongs in a deterministic, versioned, validated library that the agent calls; the agent chooses what to verify and explains the verdict.
finvariant-mcp architecture: an AI agent calls the server's check_statements and describe_schema tools, which route to the validated finvariant core and return a structured audit report (verdict, findings, provenance)
| Tool | Purpose |
|---|---|
check_statements | Verify statements against the accounting invariants; return a verdict, the failing checks with expected vs actual, counts, provenance and a plain-language summary. |
describe_schema | The canonical field names by statement, the sign convention, and the invariants checked; call it first to format the input. |
Both tools are read-only and return finvariant's JSON-safe payload, so a client can present and auto-run them safely.
Run it with uv (no install needed):
uvx finvariant-mcpor install from PyPI:
pip install finvariant-mcpAdd it to your MCP client. For example:
{
"mcpServers": {
"finvariant": {
"command": "uvx",
"args": ["finvariant-mcp"]
}
}
}If you installed with pip, use "command": "finvariant-mcp" with no args.
An agent verifying a model it just built:
describe_schema()
-> the field names, sign convention and the four invariant groups
check_statements(
periods=["FY2024"],
balance_sheet={"FY2024": {
"total_assets": 540, "total_liabilities": 158, "total_equity": 380
}},
)
-> { "ok": false,
"verdict": "FAIL - statements do not tie out",
"findings": [ { "rule_id": "EQ.accounting_equation",
"expected": 538, "actual": 540, "difference": 2 } ],
"summary": "finvariant audit - ...\n Verdict: FAIL ..." }The server is a thin, stateless wrapper. All accounting logic lives in the finvariant library, which computes the invariants from their definitions and is validated against the published statements of several real companies. The server adds the tool schema, read-only annotations and an input-schema helper so an agent can format the input and act on the result.
MIT. Written and maintained by Atakan Arikan, MSc Student at Tsinghua University and Politecnico di Milano.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.