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LinkedIn_mcp_server
Este servidor MCP utiliza la API de datos de perfil Fresh LinkedIn para obtener información del perfil de LinkedIn. Se implementa como un servidor de protocolo de contexto modelo (MCP) y expone una sola herramienta, get_profile, que acepta una URL de perfil de LinkedIn y devuelve los datos de perfil en formato JSON.
3 years
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LinkedIn Profile Scraper MCP Server
This MCP server uses the Fresh LinkedIn Profile Data API to fetch LinkedIn profile information. It is implemented as a model context protocol (MCP) server and exposes a single tool, get_profile
, which accepts a LinkedIn profile URL and returns the profile data in JSON format.
Features
- Fetch Profile Data: Retrieves LinkedIn profile information including skills and other settings (with most additional details disabled).
-
Asynchronous HTTP Requests: Uses
httpx
for non-blocking API calls. -
Environment-based Configuration: Reads the
RAPIDAPI_KEY
from your environment variables usingdotenv
.
Prerequisites
- Python 3.7+ – Ensure you are using Python version 3.7 or higher.
- MCP Framework: Make sure the MCP framework is installed.
-
Required Libraries: Install
httpx
,python-dotenv
, and other dependencies. -
RAPIDAPI_KEY: Obtain an API key from RapidAPI and add it to a
.env
file in your project directory (or set it in your environment).
Installation
-
Clone the Repository:
git clone https://github.com/codingaslu/Linkedin_Mcp_Server cd Linkedin_Mcp_Server
-
Install Dependencies:
uv add mcp[cli] httpx requests
-
Set Up Environment Variables:
Create a
.env
file in the project directory with the following content:RAPIDAPI_KEY=your_rapidapi_key_here
Running the Server
To run the MCP server, execute:
uv run linkedin.py
The server will start and listen for incoming requests via standard I/O.
MCP Client Configuration
To connect your MCP client to this server, add the following configuration to your config.json
. Adjust the paths as necessary for your environment:
{
"mcpServers": {
"linkedin_profile_scraper": {
"command": "C:/Users/aiany/.local/bin/uv",
"args": [
"--directory",
"C:/Users/aiany/OneDrive/Desktop/linkedin-mcp/project",
"run",
"linkedin.py"
]
}
}
}
Code Overview
-
Environment Setup: The server uses
dotenv
to load theRAPIDAPI_KEY
required to authenticate with the Fresh LinkedIn Profile Data API. -
API Call: The asynchronous function
get_linkedin_data
makes a GET request to the API with specified query parameters. -
MCP Tool: The
get_profile
tool wraps the API call and returns formatted JSON data, or an error message if the call fails. -
Server Execution: The MCP server is run with the
stdio
transport.
Troubleshooting
-
Missing RAPIDAPI_KEY: If the key is not set, the server will raise a
ValueError
. Make sure the key is added to your.env
file or set in your environment. - API Errors: If the API request fails, the tool will return a message indicating that the profile data could not be fetched.
License
This project is licensed under the MIT License. See the LICENSE file for more details.
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Reviews

user_fhcM8ajm
Linkedin_Mcp_Server by codingaslu is a fantastic tool for developers looking to integrate LinkedIn services effortlessly. The well-documented repository on GitHub provides clear instructions and examples that make the setup process smooth. The welcome message is informative, and the initial URL setup guides users seamlessly. Highly recommend for anyone needing a reliable LinkedIn MCP solution!