MCP 服务器

MCP Accounting

An API-based accounting analysis tool that identifies financial anomalies like unusually large transactions and duplicate payments from CSV datasets. It allows AI agents to perform automated financial auditing and transaction analysis through structured tool endpoints.

README

MCP Accounting

An AI-ready accounting anomaly detection API built with Python and FastAPI. This project demonstrates how financial analysis capabilities can be exposed as callable tools, making them usable by automation systems or AI agents.

The system analyzes accounting transactions, detects suspicious patterns, and generates AI explanations for flagged anomalies.

Overview

Traditional accounting analysis is often manual and time-consuming. This project explores a different architecture:

Expose accounting analytics as API tools that can be called by software or AI agents.

The service:

Accepts accounting datasets (CSV)
Detects potential anomalies
Generates a structured report
Uses AI to explain suspicious transactions

Current Features

✔ Upload accounting datasets via API ✔ Detect unusually large transactions ✔ Detect duplicate vendor payments ✔ Generate anomaly reports ✔ AI-generated explanations for suspicious transactions ✔ Clean modular FastAPI architecture ✔ CLI testing with curl and jq

Architecture

The backend follows a layered architecture:

HTTP API
   ↓
API Routes
   ↓
Service Layer
   ↓
Data Layer

Project structure:

mcp-accounting
│
├── app
│   ├── api
│   │   └── routes.py
│   │
│   ├── core
│   │   └── config.py
│   │
│   ├── data
│   │   └── loader.py
│   │
│   ├── mcp
│   │   └── tools.py
│   │
│   ├── models
│   │   └── schemas.py
│   │
│   ├── services
│   │   ├── anomaly_detection.py
│   │   ├── report_service.py
│   │   └── explanation_service.py
│   │
│   └── main.py
│
├── data
│   └── transactions.csv
│
├── requirements.txt
└── README.md

Installation

Clone the repository:

git clone https://github.com/<your-username>/mcp-accounting.git
cd mcp-accounting

Create a virtual environment:

python -m venv venv
source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Environment Variables

Create a .env file in the project root:

OPENAI_API_KEY=your_api_key_here

The application loads this automatically using python-dotenv.

Running the Server

Start the FastAPI server:

uvicorn app.main:app --reload

Server runs at:

http://127.0.0.1:8000

API Documentation

Swagger UI:

http://127.0.0.1:8000/docs

API Endpoints

Health Check

GET /health

Example:

curl http://127.0.0.1:8000/health

Upload Transactions Dataset

POST /upload-transactions

Example:

curl -F "file=@data/transactions.csv" \
http://127.0.0.1:8000/upload-transactions

Detect Large Transactions

POST /tools/detect_large_expenses

Example:

curl -X POST http://127.0.0.1:8000/tools/detect_large_expenses | jq

Detect Duplicate Payments

POST /tools/find_duplicate_payments

Example:

curl -X POST http://127.0.0.1:8000/tools/find_duplicate_payments | jq

Generate Anomaly Report

POST /report/anomalies

Example:

curl -X POST http://127.0.0.1:8000/report/anomalies | jq

Example response:

{
  "summary": {
    "transactions_analyzed": 5,
    "anomalies_detected": 3
  },
  "anomalies": [...]
}

Generate AI-Explained Anomaly Report

POST /report/anomalies/explain

Example:

curl -X POST http://127.0.0.1:8000/report/anomalies/explain | jq

Example output:

{
  "vendor": "Dell",
  "amount": "8200.00",
  "anomaly_type": "large_transaction",
  "ai_explanation": "This transaction is significantly higher than the vendor's typical payments and may require further review."
}

Example Dataset

date,description,vendor,amount
2025-01-01,Office Supplies,Staples,120
2025-01-05,Consulting Fee,ABC Consulting,1500
2025-01-10,Consulting Fee,ABC Consulting,1500
2025-01-15,Equipment,Dell,8200
2025-01-20,Software License,Microsoft,300