Which integrations are available for this server?

Provides a tool for scraping product search results from phonelcdparts.com using Firecrawl and BeautifulSoup, retrieving product information including name, price, URL, and image for any search query.

How do I use PhoneLCDParts MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@PhoneLCDParts MCP Server find iPhone 15 Pro LCD screens with prices" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

PhoneLCDParts MCP Server

This project provides a Model Context Protocol (MCP) server with a tool to scrape product search results from phonelcdparts.com.

Purpose

The primary tool, scrape_phonelcdparts, allows an MCP-compatible client (like an LLM agent) to query the phonelcdparts.com website for products based on a search term. It returns structured JSON data containing the product name, price, direct URL, and image URL.

This enables automated product information retrieval for various applications, such as price tracking, data analysis, or integration into larger AI-driven workflows.

Related MCP server: Vinted-scrapper

Prerequisites

Python 3.12 or higher.
uv (for environment and package management).
A valid Firecrawl API key (from firecrawl.dev).

Setup

Clone the repository (if applicable) or navigate to the project directory:
cd path/to/phonelcdpart-mcp
Create and activate a virtual environment using
uv venv source .venv/bin/activate
Configure Firecrawl API Key: Create a file named .env in the phonelcdpart-mcp project root directory (i.e., phonelcdpart-mcp/.env). Add your Firecrawl API key to this file:
FIRECRAWL_API_KEY="YOUR_ACTUAL_FIRECRAWL_API_KEY_HERE"
The application uses the python-dotenv library to load this key at runtime.
Install dependencies using
uv pip install .
This will install all dependencies listed in pyproject.toml, including python-dotenv.

Running the MCP Server

You have a few options to run the server:

Directly using Python (for simple development):
python app.py
Using Uvicorn (recommended for development, provides auto-reload): Ensure uvicorn is installed (it's in pyproject.toml).
uvicorn app:mcp --reload --host 0.0.0.0 --port 8000
(The app:mcp refers to the mcp instance of FastMCP in your app.py file.)
Using the installed script (if After a successful uv pip install ., a script defined in pyproject.toml should be available:
start-mcp
This will typically use the mcp.run() method.

The server will usually start on http://127.0.0.1:8000 or http://0.0.0.0:8000.

Using the Tool

Once the server is running, you can interact with it using any MCP-compatible client.

Tool Name: scrape_phonelcdparts
Description (from docstring): Scrapes product information (name, price, URL, image URL) from phonelcdparts.com for a given search query.
Argument:
- search_query (string): The product search term (e.g., "iphone 15 pro max lcd").
Returns: A list of dictionaries, where each dictionary contains:
- name (string)
- price (string)
- url (string)
- image_url (string)

Example Call (conceptual, using a Python client):

# (This is a conceptual example of how a client might call the tool) # import asyncio # from fastmcp import Client # # async def main(): # # Ensure the server_url matches where your MCP server is running # server_url = "http://127.0.0.1:8000/sse" # async with Client(server_url) as client: # try: # result = await client.call_tool( # "scrape_phonelcdparts", # {"search_query": "iphone 14 screen"} # ) # if result and result.data: # print("Tool Result:") # for item in result.data: # print(item) # else: # print("No data returned or tool call failed.") # except Exception as e: # print(f"Error calling tool: {e}") # # if __name__ == "__main__": # asyncio.run(main())

This client code would connect to your running MCP server and invoke the scrape_phonelcdparts tool with the specified search query, then print the structured JSON results.