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Ayesha0300

LinkedIn Profile Scraper MCP Server

by Ayesha0300
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Python
Remote

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 using dotenv.

Related MCP server: linkedin-mcp-server

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

  1. Clone the Repository:

    git clone https://github.com/AIAnytime/Awesome-MCP-Server
cd linkedin_profile_scraper

  2. Install Dependencies:

    uv add mcp[cli] httpx requests

  3. 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/YT Video/linkedin-mcp/project",
        "run",
        "linkedin.py"
      ]
    }
  }
}

Code Overview

  • Environment Setup: The server uses dotenv to load the RAPIDAPI_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.

Available Tools

1 tool
get_profileB

Get LinkedIn profile data for a given profile URL.

Args:
    linkedin_url: The LinkedIn profile URL.
ParametersJSON Schema
NameRequiredDescriptionDefault
linkedin_urlYes

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions retrieving data but lacks details on permissions, rate limits, error handling, or output format. This is a significant gap for a tool with zero annotation coverage, though it at least correctly implies a read operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by a structured Args section. It avoids redundancy and is appropriately sized for a single-parameter tool, though the formatting could be slightly more polished.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter but lacks details on behavioral traits and output, which are important for an agent to use it effectively without annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With only one parameter and 0% schema description coverage, the description compensates by explaining the parameter's purpose ('The LinkedIn profile URL') in the Args section. This adds meaningful context beyond the bare schema, though it could specify format constraints (e.g., URL validation).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('LinkedIn profile data'), and specifies the input requirement ('for a given profile URL'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. It only states what the tool does, not when it should be applied, leaving usage decisions entirely to the agent's inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.2/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern (get_profile), and with only one tool, consistency is inherently perfect as there are no other tools to compare against.

Tool Count2/5

A single tool is too few for a server labeled as a 'LinkedIn Profile Scraper MCP Server,' which suggests a broader scope. This minimal set feels thin and underdeveloped for scraping tasks that might include multiple operations like search, batch processing, or data extraction beyond single profiles.

Completeness2/5

The tool surface is severely incomplete for a LinkedIn scraper. It only allows fetching a single profile by URL, missing essential operations such as searching for profiles, handling authentication, pagination, or extracting additional data like connections or posts, which are typical in scraping workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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