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dingdawg-finance-agent

dingdawg-finance-agent

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github.com/dingdawgFinance1Updated 29w ago
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About dingdawg-finance-agent

dingdawg-finance-agent is an MCP server for AI assistants that provides financial analysis, forecasting, compliance checks, expense categorization, and risk assessment. It returns structured JSON and attaches an auditable governance receipt to each call.

Key features

  1. 01Financial ratios with industry benchmarks
  2. 02Budget forecasts and scenario modeling
  3. 03SOX/GAAP checks with control gaps
  4. 04Expense categorization and anomaly detection
  5. 05Risk scoring and Monte Carlo simulation

Use cases

  1. 01Reviewing financial ratios against benchmarks
  2. 02Projecting budgets across scenarios
  3. 03Preparing SOX/GAAP compliance checks
  4. 04Categorizing expenses and assessing exposure

MCP support

The MCP server exposes five financial tools: financial_analysis, budget_forecast, compliance_check, expense_categorize, and risk_assessment. Each call returns structured JSON with a governance receipt.

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-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-agent2.0.7

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

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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