Metadata-Version: 2.4
Name: linkedin-mcp
Version: 0.1.0
Summary: Add your description here
Author-email: Hritik Raj <hritikraj_03@outlook.com>
Project-URL: Homepage, https://github.com/Hritik003/linkedin-mcp
Requires-Python: >=3.12
Description-Content-Type: text/markdown
Requires-Dist: fastmcp>=0.4.1
Requires-Dist: linkedin-api>=2.3.1
Requires-Dist: python-dotenv>=1.0.1
Requires-Dist: requests>=2.32.3
Requires-Dist: uvicorn>=0.34.0
# MCP Server for LinkedIn
[](https://smithery.ai/server/@Hritik003/linkedin-mcp)
A Model Context Protocol (MCP) server for linkedin to apply Jobs and search through feed seamlessly.
This uses Unoffical [Linkedin API Docs](https://linkedin-api.readthedocs.io/en/latest/api.html) for hitting at the clients Credentials.
## Features
1. **Profile Retrieval**
Fetch user profiles using `get_profile()` function
Extract key information such as `name`, `headline`, and `current position`
2. **Job Search**
- Advanced job search functionality with multiple parameters:
- Keywords
- Location
- Experience level
- Job type (Full-time, Contract, Part-time)
- Remote work options
- Date posted
- Required skills
- Customizable search limit
3. **Feed Posts**
- Retrieve LinkedIn feed posts using `get_feed_posts()`
- Configurable limit and offset for pagination
4. **Resume Analysis**
- Parse and extract information from `resumes (PDF format)`
- Extracted data includes:
- Name
- Email
- Phone number
- Skills
- Work experience
- Education
- Languages
---
# Configuration
After cloning the repo, adjust the `<LOCAL_PATH>` accordingly
```python
{
"linkedin":{
"command":"uv",
"args": [
"--directory",
"<LOCAL_PATH>",
"run",
"linkedin.py"
]
}
}
```
---
# Usage
I have been testing using [MCP-client](https://github.com/chrishayuk/mcp-cli) and found as the best one for testing your `MCP-Servers`.
