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dingdawg-finance-agent

dingdawg-finance-agent

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github.com/DingDawg-devFinance1Updated 29w ago
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About dingdawg-finance-agent

dingdawg-finance-agent is an MCP server for financial analysis, forecasting, expense categorization, and risk assessment. Its tools return structured results with governance receipts, with capabilities varying by tier.

Key features

  1. 01Financial ratio analysis
  2. 02Budget projections and scenarios
  3. 03SOX/GAAP compliance checks
  4. 04Expense categorization
  5. 05Risk scoring and simulation

Use cases

  1. 01Reviewing financial ratios against benchmarks
  2. 02Projecting budgets under multiple scenarios
  3. 03Checking SOX/GAAP control gaps
  4. 04Categorizing expenses and identifying anomalies

MCP support

Exposes five financial analysis tools to MCP clients, returning structured JSON. Each call includes a governance receipt for auditing.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "dingdawg-dev-dingdawg-finance-agent": {
      "command": "npx",
      "args": [
        "-y",
        "dingdawg-finance-agent"
      ],
      "env": {
        "DINGDAWG_API_KEY": "<DINGDAWG_API_KEY>"
      }
    }
  }
}
Run commandnpx -y dingdawg-finance-agent
Environment variables to set
DINGDAWG_API_KEYSecretAPI key for paid tier access — get free at dingdawg.com

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmdingdawg-finance-agent2.0.4

Tools

5

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
cashnumberNoCash and equivalents
assetsnumberNoTotal assets
revenuenumberNoAnnual revenue
expensesnumberNoAnnual expenses
descriptionstringNoFree-text financial situation description
liabilitiesnumberNoTotal liabilities

Details

From the project README

dingdawg-finance-agent

Breakthrough financial analysis bottlenecks. AI finance that learns YOUR business metrics.

AI-powered financial analysis, budget forecasting, compliance checking, expense categorization, and risk assessment. Free tier runs ratio analysis locally. Paid tier provides LLM-powered financial modeling with SOX and GAAP compliance tracking. Every action is governed and receipted.

For AI Assistants

This MCP server returns structured JSON for seamless integration:

  • Financial ratios (liquidity, profitability, solvency) with industry benchmarks
  • SOX/GAAP compliance status with specific control gap identification
  • Governance receipt on every call (audit-ready)
  • Chain-ready: financial_analysis for baseline -> budget_forecast for projections -> compliance_check for SOX/GAAP -> expense_categorize for line items -> risk_assessment for exposure

Composable with any MCP client: Claude Code, Cursor, VS Code, ChatGPT Desktop, Windsurf.

Install

npx dingdawg-finance-agent
Claude Code
claude mcp add finance -- npx dingdawg-finance-agent
Cursor

Add to .cursor/mcp.json:

{"mcpServers": {"finance": {"command": "npx", "args": ["dingdawg-finance-agent"], "env": {"DINGDAWG_API_KEY": "your-key"}}}}
Full Stack (all 13 agents)
npx dingdawg-setup

Tools

ToolFree TierPaid Tier
financial_analysis10/day, basic ratio analysisUnlimited, LLM-powered with industry benchmarking
budget_forecast5/day, linear projectionUnlimited, AI-powered multi-scenario modeling
compliance_check5/day, checklist-based SOX/GAAPUnlimited, deep compliance with control gap analysis
expense_categorize20/day, rule-based categorizationUnlimited, AI-powered with anomaly detection
risk_assessment5/day, basic risk scoringUnlimited, Monte Carlo simulation with risk factors

Pricing

  • Free: 10 analyses/day, basic ratios
  • Pro: $49/mo, 100 calls/day, AI-powered deep analysis
  • Pay-as-you-go: $0.25/call, no commitment

Get API key: https://dingdawg.com/developers

Governed

Every call is receipted and auditable. Financial analyses reference GAAP standards and SOX control requirements. Budget forecasts include methodology disclosure. Risk assessments include confidence intervals and assumption documentation.

Support

[email protected] | https://dingdawg.com

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