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).

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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Tracking	Track applications locally with status workflow (interested → applied → interviewing → offered/rejected/withdrawn)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)

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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"
  }
}

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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

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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

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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)

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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.

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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"

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License

MIT