Content
# LinkedIn MCP Server
A [Model Context Protocol](https://modelcontextprotocol.io/) (MCP) server for LinkedIn. Search people, companies, and jobs, scrape profiles, and retrieve structured JSON data from any MCP-compatible AI client.
https://github.com/user-attachments/assets/50cd8629-41ee-4261-9538-40dc7d30294e
Built with [FastMCP](https://github.com/PrefectHQ/fastmcp), [Patchright](https://github.com/Kaliiiiiiiiii-Vinyzu/patchright), and a clean hexagonal architecture.
---
## Features
| Category | Tools |
| ----------- | ---------------------------------------- |
| People | `get_person_profile` · `search_people` |
| Companies | `get_company_profile` · `get_company_posts` |
| Jobs | `get_job_details` · `search_jobs` |
| Browser | `close_browser` |
### Person Profile Sections
The `get_person_profile` tool supports granular section scraping. Request only the sections you need:
- **Main profile** (always included) — name, headline, location, followers, connections, about, profile image
- **Experience** — title, company, dates, duration, description, company logo
- **Education** — school, degree, dates, description, school logo
- **Contact info** — email, phone, websites, birthday, LinkedIn URL
- **Interests** — people, companies, and groups followed
- **Honors and awards** — title, issuer, description
- **Languages** — language name and proficiency level
- **Posts** — recent activity with reactions and timestamps
- **Recommendations** — received and given, with author details
### Company Profile Sections
- **About** (always included) — overview, website, industry, size, headquarters, specialties, logo
- **Posts** — recent feed posts with engagement metrics
- **Jobs** — current open positions
### Job Search Filters
The `search_jobs` tool supports the following filters:
| Filter | Values |
| ------------------ | ------------------------------------------------------------------------- |
| `date_posted` | `past_hour`, `past_24_hours`, `past_week`, `past_month` |
| `job_type` | `full_time`, `part_time`, `contract`, `temporary`, `internship`, `other` |
| `experience_level` | `entry`, `associate`, `mid_senior`, `director`, `executive` |
| `work_type` | `on_site`, `remote`, `hybrid` |
| `easy_apply` | `true` / `false` |
| `sort_by` | `date`, `relevance` |
---
## Prerequisites
- Python 3.12 or later
- [uv](https://docs.astral.sh/uv/) package manager
- A LinkedIn account for authentication
---
## Quick Start
### 1. Clone and install
```bash
git clone https://github.com/eliasbiondo/linkedin-mcp-server.git
cd linkedin-mcp-server
uv sync
```
### 2. Install browser
This project uses [Patchright](https://github.com/Kaliiiiiiiiii-Vinyzu/patchright) (a patched fork of Playwright) for browser automation. You need to install the browser binaries before first use:
```bash
uv run patchright install
```
> **Windows users:** If the command above fails with `program not found`, run instead:
>
> ```powershell
> uv run python -m patchright install
> ```
### 3. Authenticate with LinkedIn
```bash
uv run linkedin-mcp-server --login
```
A browser window will open. Log in to LinkedIn and the session will be persisted locally at `~/.linkedin-mcp-server/browser-data`.
### 4. Run the server
**stdio transport** (default — for Claude Desktop, Cursor, and similar clients):
```bash
uv run linkedin-mcp-server
```
**HTTP transport** (for remote clients, the MCP Inspector, etc.):
```bash
uv run linkedin-mcp-server --transport streamable-http --host 0.0.0.0 --port 8000
```
---
## Client Integration
### Claude Desktop / Cursor
Add to your MCP configuration file:
```json
{
"mcpServers": {
"linkedin": {
"command": "uv",
"args": [
"--directory", "/path/to/linkedin-mcp-server",
"run", "linkedin-mcp-server"
]
}
}
}
```
### MCP Inspector
```bash
npx @modelcontextprotocol/inspector
```
Then connect to `http://localhost:8000/mcp` if using HTTP transport.
---
## Configuration
Configuration follows a strict precedence chain: **CLI args > environment variables > `.env` file > defaults**.
### CLI Arguments
| Argument | Description | Default |
| --------------- | ----------------------------------- | ----------- |
| `--transport` | `stdio` or `streamable-http` | `stdio` |
| `--host` | Host for HTTP transport | `127.0.0.1` |
| `--port` | Port for HTTP transport | `8000` |
| `--log-level` | `DEBUG`, `INFO`, `WARNING`, `ERROR` | `WARNING` |
| `--headless` | Run browser in headless mode | `true` |
| `--no-headless` | Show browser window (visible mode) | — |
| `--login` | Open browser for LinkedIn login | — |
| `--logout` | Clear stored credentials | — |
| `--status` | Check session status | — |
### Environment Variables
Create a `.env` file in the project root:
```env
# Server
LINKEDIN_TRANSPORT=stdio
LINKEDIN_HOST=127.0.0.1
LINKEDIN_PORT=8000
LINKEDIN_LOG_LEVEL=WARNING
# Browser
LINKEDIN_HEADLESS=true
LINKEDIN_SLOW_MO=0
LINKEDIN_TIMEOUT=10000
LINKEDIN_VIEWPORT_WIDTH=1280
LINKEDIN_VIEWPORT_HEIGHT=720
LINKEDIN_CHROME_PATH=
LINKEDIN_USER_AGENT=
LINKEDIN_USER_DATA_DIR=~/.linkedin-mcp-server/browser-data
```
---
## Architecture
The project follows a hexagonal (ports and adapters) architecture with strict layer separation:
```
src/linkedin_mcp_server/
├── domain/ # Core business logic — zero external dependencies
│ ├── models/ # Data models (Person, Company, Job, Search)
│ ├── parsers/ # HTML to structured data parsers
│ ├── exceptions.py # Domain exceptions
│ └── value_objects.py # Immutable configuration and content objects
├── ports/ # Abstract interfaces
│ ├── auth.py # Authentication port
│ ├── browser.py # Browser automation port
│ └── config.py # Configuration port
├── application/ # Use cases — orchestration layer
│ ├── scrape_person.py
│ ├── scrape_company.py
│ ├── scrape_job.py
│ ├── search_people.py
│ ├── search_jobs.py
│ └── manage_session.py
├── adapters/ # Concrete implementations
│ ├── driven/ # Infrastructure adapters (browser, auth, config)
│ └── driving/ # Interface adapters (CLI, MCP tools, serialization)
└── container.py # Dependency injection composition root
```
### Design Decisions
- **Ports and adapters** — Domain logic is fully decoupled from infrastructure. The browser engine, MCP framework, and configuration source can all be swapped independently.
- **Dependency injection** — A single `Container` class acts as the composition root and is the only place that imports concrete adapter classes.
- **Structured JSON output** — LinkedIn HTML is parsed into typed Python dataclasses, then serialized to JSON for reliable LLM consumption.
- **Session persistence** — Browser state is saved to disk, so authentication is required only once.
---
## Development
### Setup
```bash
uv sync --group dev
uv run pre-commit install
```
### Running tests
```bash
uv run pytest
```
With coverage:
```bash
uv run pytest --cov=linkedin_mcp_server
```
### Linting and formatting
This project uses [Ruff](https://docs.astral.sh/ruff/) for both linting and formatting. Pre-commit hooks will run these automatically on each commit.
```bash
# Lint
uv run ruff check .
# Lint and auto-fix
uv run ruff check . --fix
# Format
uv run ruff format .
```
---
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
## Contributing
Contributions are welcome. Please read the [contributing guide](CONTRIBUTING.md) for details on the development workflow and submission process.
---
## Disclaimer
This tool is intended for personal and educational use. Scraping LinkedIn may violate their Terms of Service. Use responsibly and at your own risk. The authors are not responsible for any misuse or consequences arising from the use of this software.
