# 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.