# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Development Commands
**Environment Setup:**
- Use `uv` for dependency management: `uv sync` (installs all dependencies)
- Development dependencies: `uv sync --group dev`
- Bump version: `uv version --bump minor` (or `major`, `patch`) - git tag is created automatically by release workflow. Once Docker image is published, manually file a PR in the MCP registry to update the version.
- Run server locally: `uv run -m linkedin_mcp_server --no-headless`
- Run in Docker: `docker run -it --rm -v ~/.linkedin-mcp:/home/pwuser/.linkedin-mcp stickerdaniel/linkedin-mcp-server:latest`
**Code Quality:**
- Lint: `uv run ruff check .` (auto-fix with `--fix`)
- Format: `uv run ruff format .`
- Type check: `uv run ty check` (using ty, not mypy)
- Tests: `uv run pytest` (with coverage: `uv run pytest --cov`)
- Pre-commit hooks: `uv run pre-commit install` then `uv run pre-commit run --all-files`
**Docker Commands:**
- Build: `docker build -t linkedin-mcp-server .`
- Get session: Use uvx locally first: `uvx --from git+https://github.com/stickerdaniel/linkedin-mcp-server linkedin-mcp-server --get-session`
## Architecture Overview
This is a **LinkedIn MCP (Model Context Protocol) Server** that enables AI assistants to interact with LinkedIn through web scraping. The codebase follows a two-phase startup pattern:
1. **Authentication Phase** (`authentication.py`) - Validates LinkedIn session file exists
2. **Server Runtime Phase** (`server.py`) - Runs FastMCP server with tool registration
**Core Components:**
- `cli_main.py` - Entry point with CLI argument parsing and orchestration
- `server.py` - FastMCP server setup and tool registration
- `tools/` - LinkedIn scraping tools (person, company, job profiles)
- `drivers/browser.py` - Playwright browser management with session handling
- `config/` - Configuration management (schema, loaders)
- `authentication.py` - LinkedIn session management
**Tool Categories:**
- **Person Tools** (`tools/person.py`) - Profile scraping with contacts, interests, experiences, education
- **Company Tools** (`tools/company.py`) - Company profile and posts extraction
- **Job Tools** (`tools/job.py`) - Job posting details and search functionality
**Available MCP Tools:**
| Tool | Description |
|------|-------------|
| `get_person_profile` | Get profile with contacts (email/phone/social), interests, experiences, education |
| `get_company_profile` | Get company info with employees, affiliated companies, showcase pages |
| `get_company_posts` | Get recent posts from company feed with reactions/comments/images |
| `get_job_details` | Get job posting details including description and benefits |
| `search_jobs` | Search jobs by keywords and location |
| `close_session` | Close browser session and clean up resources |
**Authentication Flow:**
- Uses session files stored at `~/.linkedin-mcp/session.json`
- Run with `--get-session` to create a session via browser login
**Transport Modes:**
- `stdio` (default) - Standard I/O for CLI MCP clients
- `streamable-http` - HTTP server mode for web-based MCP clients
## Development Notes
- **Python Version:** Requires Python 3.12+
- **Package Manager:** Uses `uv` for fast dependency resolution
- **Browser:** Uses Playwright with Chromium for browser automation
- **Logging:** Configurable levels, JSON format for non-interactive mode
- **Error Handling:** Comprehensive exception handling for LinkedIn rate limits, captchas, etc.
**Key Dependencies:**
- `fastmcp` - MCP server framework
- `linkedin_scraper` - LinkedIn web scraping (v3 with Playwright)
- `playwright` - Browser automation
**Configuration:**
- CLI arguments with comprehensive help (`--help`)
- Session stored at `~/.linkedin-mcp/session.json`
**Commit Message Format:**
- Follow conventional commits: `type(scope): subject`
- Types: feat, fix, docs, style, refactor, test, chore, perf, ci
- Keep subject <50 chars, imperative mood
## Commit Message Guidelines
**Commit Message Rules:**
- Always use the commit message format type(scope): subject
- Types: feat, fix, docs, style, refactor, test, chore, perf, ci
- Keep subject <50 chars, imperative mood
## Important Development Notes
### Development Workflow
- Never sign a PR or commit with Claude Code
- When implementing a new feature/fix, follow this process:
1. Check open issues. If no issue exists for the feature, create one that follows the feature issue template.
2. Create a new branch from `main` and name it `feature/issue-number-short-description`
3. Implement the feature
4. Test the feature
5. Make sure the README.md, docs/docker-hub.md and AGENTS.md is updated with the new feature
6. Create a PR with a short description of the feature/fix
7. First review the PR with ai agents.
8. Manually review the PR and merge it if it's approved. Do not squash the commits.
9. Delete the branch after the PR is merged.
## btca
When you need up-to-date information about technologies used in this project, use btca to query source repositories directly.
**Available resources**: fastmcp, linkedinScraper, playwright, pytest, ruff, ty, uv, inquirer, pythonDotenv, pyperclip, preCommit
### Usage
```bash
btca ask -r <resource> -q "<question>"
```
Use multiple `-r` flags to query multiple resources at once:
```bash
btca ask -r fastmcp -r playwright -q "How do I set up browser context with FastMCP tools?"
```
