LinkedIn MCP Server

An MCP server that gives AI assistants full access to LinkedIn — search jobs, view profiles and companies, generate AI-powered resumes and cover letters, and track applications. Built with the official MCP Python SDK (FastMCP).

---

What It Does

| Category | Capabilities |
|---|---|
| Job Search | Search with filters (keywords, location, type, experience level, remote, recency), get job details, get recommendations |
| Profiles & Companies | Fetch any LinkedIn profile or company page, AI-powered profile analysis with optimization suggestions |
| Resume Generation | Generate resumes from LinkedIn profiles, tailor resumes to specific job postings, 3 built-in templates |
| Cover Letters | AI-generated cover letters personalized to each job, 2 built-in templates |
| Application Tracking | Track applications locally with status workflow (interested → applied → interviewing → offered/rejected/withdrawn) |
| Output Formats | HTML, Markdown, and PDF (via WeasyPrint) |

---

Quick Start

1. Install

# Core installation
pip install -e .
With AI features (resume/cover letter generation, profile analysis)
pip install -e ".[ai]"
With PDF export
pip install -e ".[pdf]"
Everything
pip install -e ".[all]"

2. Configure

cp .env.example .env

Edit .env with your credentials:

LINKEDIN_USERNAME=your_email@example.com
LINKEDIN_PASSWORD=your_password
ANTHROPIC_API_KEY=sk-ant-...    # Optional — enables AI features

3. Run

Standalone:

linkedin-mcp

With Claude Desktop — add to your claude_desktop_config.json:

{
  "mcpServers": {
    "linkedin": {
      "command": "linkedin-mcp"
    }
  }
}

With Claude Code — add to .mcp.json:

{
  "linkedin": {
    "command": "linkedin-mcp"
  }
}

---

Tools Reference

Job Tools (3)

| Tool | Parameters | Description |
|------|-----------|-------------|
| search_jobs | keywords, location, job_type, experience_level, remote, date_posted, page, count | Search LinkedIn jobs with rich filters |
| get_job_details | job_id | Get full description, skills, and metadata for a job posting |
| get_recommended_jobs | count | Get personalized job recommendations |

Profile Tools (3)

| Tool | Parameters | Description |
|------|-----------|-------------|
| get_profile | profile_id | Fetch a LinkedIn profile ("me" for your own) — experience, education, skills |
| get_company | company_id | Get company info — description, size, headquarters, specialties |
| analyze_profile | profile_id | AI-powered profile review with actionable optimization suggestions |

Document Generation Tools (4)

| Tool | Parameters | Description |
|------|-----------|-------------|
| generate_resume | profile_id, template, output_format | Generate a resume from a LinkedIn profile |
| tailor_resume | profile_id, job_id, template, output_format | Generate a resume tailored to a specific job posting |
| generate_cover_letter | profile_id, job_id, template, output_format | Create a personalized cover letter for a job |
| list_templates | template_type | List available templates (resume, cover_letter, or all) |

Templates: modern · professional · minimal (resume) | professional · concise (cover letter)
Formats: html · md · pdf

Application Tracking Tools (3)

| Tool | Parameters | Description |
|------|-----------|-------------|
| track_application | job_id, job_title, company, status, notes, url | Start tracking a job application |
| list_applications | status | List all tracked applications, optionally filtered by status |
| update_application_status | job_id, status, notes | Update application status |

Status values: interested · applied · interviewing · offered · rejected · withdrawn

---

Architecture

src/linkedin_mcp/
├── server.py                    # FastMCP entry point — 13 tools, 1 resource
├── config.py                    # Settings from .env (frozen dataclass)
├── exceptions.py                # 7-class exception hierarchy
├── models/
│   ├── linkedin.py              # Profile, Job, Company models (Pydantic v2)
│   ├── resume.py                # Resume & cover letter content models
│   └── tracking.py              # Application tracking model
├── services/
│   ├── linkedin_client.py       # LinkedIn API wrapper (async via asyncio.to_thread)
│   ├── job_search.py            # Job search with TTL caching
│   ├── profile.py               # Profile/company access with caching
│   ├── resume_generator.py      # AI-enhanced resume generation
│   ├── cover_letter_generator.py
│   ├── application_tracker.py   # Local JSON-based application tracking
│   ├── cache.py                 # Unified JSON file cache with TTL
│   ├── template_manager.py      # Jinja2 sandboxed template engine
│   └── format_converter.py      # HTML → PDF/Markdown conversion
├── ai/
│   ├── base.py                  # Abstract AI provider interface
│   └── claude_provider.py       # Anthropic Claude implementation
└── templates/
    ├── resume/                  # modern.j2, professional.j2, minimal.j2
    └── cover_letter/            # professional.j2, concise.j2

Key Design Decisions

- Official MCP SDK — Uses FastMCP with @mcp.tool() decorators, not a custom protocol implementation
- Async throughout — All sync LinkedIn API calls wrapped in asyncio.to_thread() to avoid blocking
- Layered architecture — Tools → Services → Client, with caching at the service layer
- AI is optional — Core LinkedIn features work without an Anthropic API key; AI enhances resume/cover letter generation
- Security hardened — Jinja2 SandboxedEnvironment, WeasyPrint SSRF protection, path traversal guards, credential redaction, input validation

---

Configuration

All settings are loaded from environment variables (.env file supported):

| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| LINKEDIN_USERNAME | Yes | — | Your LinkedIn email |
| LINKEDIN_PASSWORD | Yes | — | Your LinkedIn password |
| ANTHROPIC_API_KEY | No | — | Enables AI features (resume/cover letter generation, profile analysis) |
| AI_MODEL | No | claude-sonnet-4-20250514 | Claude model to use |
| DATA_DIR | No | ~/.linkedin_mcp/data | Directory for cache, tracking data, generated files |
| CACHE_TTL_HOURS | No | 24 | How long to cache LinkedIn API responses |
| LOG_LEVEL | No | INFO | Logging level (DEBUG, INFO, WARNING, ERROR) |

---

Development

# Install with all dependencies
pip install -e ".[all,dev]"
Run tests (82 tests)
pytest
Run with coverage
pytest --cov=linkedin_mcp
Lint
ruff check src/ tests/

Test Coverage

Tests cover all layers: config, models, services (cache, tracker, job search, profile, resume/cover letter generation, LinkedIn client formatters, format converter), AI provider, and MCP tool handlers.

---

Usage Examples

Once connected, ask your AI assistant:

> "Search for remote Python developer jobs in the US"

> "Show me the profile for satyanadella"

> "Generate a resume from my LinkedIn profile tailored to job 3847291056"

> "Create a cover letter for job 3847291056 using the concise template"

> "Track my application for the Senior Engineer role at Google — status: applied"

> "List all my applications that are in the interviewing stage"

> "Analyze my LinkedIn profile and suggest improvements"

---

License

MIT