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Price Monitor MCP Server

by moongzee
GitHub
Python
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Integrations

  • Manages environment variables for configuration settings like database credentials and Slack webhook URLs

  • Used for data validation and settings management in the price monitoring server

  • Provides a Python client interface to interact with the MCP server, allowing programmatic access to price monitoring workflows and tools

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

  1. DB standard price search : Search the standard price in DB using the product code
  2. Gmarket Real-time Price Crawling : Using Firecrawl API
  3. Compare prices and calculate discount rates
  4. Send Slack notifications when price drops
  5. 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)

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

Contact/Contribution

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

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

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.

  1. outline
    1. Key Features
      1. Folder structure
        1. How to run
          1. Prepare virtual environment and install package
          2. Setting environment variables
          3. Run the MCP server
        2. MCP Tools/Prompts List
          1. Automate the entire process (recommended)
            1. Example of calling a workflow tool
          2. Slack notification test
            1. LLM (Claude, GPT, etc.) linkage
              1. References/Documents
                1. Contact/Contribution

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