Price Monitor MCP Server

outline

This project is a price monitoring server based on Model Context Protocol (MCP). It compares the DB standard price and Gmarket real-time price using product codes, and sends a notification to Slack when the price drops.

Server/tool/prompt structure following MCP standards
Automate the entire process of crawling, price comparison, and notifications
Supports Slack webhook integration

Key Features

DB standard price search : Search the standard price in DB using the product code
Gmarket Real-time Price Crawling : Using Firecrawl API
Compare prices and calculate discount rates
Send Slack notifications when price drops
Provides a tool to automate the entire workflow

Folder structure

price_monitor_mcp/
├── src/
│   └── price_monitor_mcp.py   # MCP 서버 메인 코드
├── mcp_client.py              # MCP 클라이언트 코드
├── README.md                  
└── .env                       # 환경변수

How to run

1. Prepare virtual environment and install package

conda activate price_monitor_mcp
pip install -r requirements.txt
# 또는 필요한 경우
pip install mcp firecrawl requests python-dotenv psycopg2-binary pydantic

2. Setting environment variables

Set environment variables such as Slack webhooks in .env file as follows.

SLACK_WEBHOOK_URL=https://hooks.slack.com/services/...
DB_HOST=...
DB_PORT=...
DB_NAME=...
DB_USER=...
DB_PASSWORD=...

3. Run the MCP server

mcp run src/price_monitor_mcp.py

Or run it with devtools: mcp dev src/price_monitor_mcp.py
Or activate the conda environment with a shell script and then run

MCP Tools/Prompts List

get_db_price(product_code) : DB standard price query
crawl_gmarket_price(product_code) : Gmarket real-time price crawling
send_slack_alert(message) : Send Slack notification
monitor_price_workflow(product_code) : Automatically run the entire process (recommended)
monitor_price(product_code) : prompt (for LLM)

Automate the entire process (recommended)

Example of calling a workflow tool

In the MCP dev tools/client:

Select the monitor_price_workflow tool, enter product_code , and run it.
Results: Returns DB price, lowest price, price difference, discount rate, Slack notification, etc.

Python client example

import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def main():
    server_params = StdioServerParameters(
        command="python",
        args=["src/price_monitor_mcp.py"],
    )
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()
            result = await session.call_tool("monitor_price_workflow", arguments={"product_code": "ULCK25151"})
            print("워크플로우 결과:", result)

if __name__ == "__main__":
    asyncio.run(main())

Slack notification test

If your Slack webhook is set up correctly, you will automatically be notified when a price drops.
The message format can be freely modified in send_slack_alert function.

LLM (Claude, GPT, etc.) linkage

Claude, GPT, etc. will be able to run prompts/tools in natural language once MCP server connection is officially supported
Currently, the results are received through MCP client code and pasted to LLM for summary/analysis request.

References/Documents

MCP Python SDK Official Documentation
Firecrawl, Slack API, DB, etc. need to be set up for each environment.

Contact/Contribution

Please leave any questions, bugs, or extension requests as issues!

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Monitors product prices by comparing database reference prices with real-time G-Market prices and sends Slack notifications when prices drop.

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