LinkedIn MCP Server — Remote Edition (Claude + ChatGPT)

An MCP server exposing LinkedIn actions, reachable from both Claude and ChatGPT as a custom connector. Runs over HTTP (not stdio) because ChatGPT only supports remote, HTTPS-reachable MCP servers — it cannot launch a local script the way Claude Desktop can.

What this can and can't do

LinkedIn's public API does not expose any endpoint for a third-party app to write directly to a profile's About, Experience, or Projects sections — for any account type. That's a platform restriction, not something this code can work around without violating LinkedIn's User Agreement (browser automation/credential scraping), which this project does not do.

generate_resume_pdf is the practical route to "updating" Experience/Education: LinkedIn auto-parses a resume PDF and pre-fills those sections for review. About and Projects aren't reliably auto-parsed — the PDF still includes them clearly labeled so you have polished text ready to paste in manually.

Related MCP server: linkedin-mcp-pro

Two tokens, don't mix them up

1. LINKEDIN_ACCESS_TOKEN — LinkedIn's own OAuth token. This server uses it to call LinkedIn's API on your behalf. Obtained once via auth_setup.py.

2. MCP_BEARER_TOKEN — a secret you generate (e.g. openssl rand -hex 32). Claude and ChatGPT send this as Authorization: Bearer <token> on every call. Without it, anyone who finds your server's URL could post to your LinkedIn or read your profile. Treat it like a password.

1. LinkedIn app setup

1. Create an app: https://www.linkedin.com/developers/apps

2. Auth tab → add redirect URL: http://localhost:8765/callback

3. Products tab → request:

   • Sign In with LinkedIn using OpenID Connect (auto-approved)

   • Share on LinkedIn (auto-approved)

4. Copy the Client ID / Secret from the Auth tab.

2. Local setup & one-time LinkedIn auth

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
# fill in LINKEDIN_CLIENT_ID, LINKEDIN_CLIENT_SECRET, and set a random MCP_BEARER_TOKEN
python auth_setup.py   # opens browser, completes LinkedIn OAuth, saves LINKEDIN_ACCESS_TOKEN to .env

3. Deploy it somewhere public

Both Claude and ChatGPT need to reach this server over HTTPS — localhost won't work for either unless you tunnel it. Pick one:

Quick test (ngrok tunnel, temporary URL):

python server.py                    # starts on http://0.0.0.0:8000
ngrok http 8000                     # gives you a temporary https:// URL

Permanent (Docker, deploy to Render/Fly.io/Railway/any host that runs containers):

docker build -t linkedin-mcp .
docker run -p 8000:8000 --env-file .env linkedin-mcp

Point that platform's generated HTTPS domain at container port 8000, path /mcp.

A Procfile is also included for platforms that build directly from source (Railway, Heroku-style) without a Dockerfile.

Your MCP endpoint URL will be: https://<your-domain>/mcp

4. Connect it to Claude

Claude.ai / Claude Desktop → Settings → Connectors → Add custom connector:

• URL: https://<your-domain>/mcp

• Authorization: Bearer token → paste your MCP_BEARER_TOKEN

5. Connect it to ChatGPT

ChatGPT → Settings → Apps & Connectors → Advanced settings → enable Developer mode (requires Plus/Pro/Business/Enterprise) → Add custom connector:

• URL: https://<your-domain>/mcp

• Authentication: choose token/API key auth → paste your MCP_BEARER_TOKEN

• In a new chat, open the + menu → Developer mode → toggle this connector on for that conversation (ChatGPT enables connectors per-conversation).

Using generate_resume_pdf

generate_resume_pdf(
    output_path="/tmp/resume.pdf",
    full_name="Vaibhav Sharma",
    headline="Frontend / Full-Stack Developer",
    email="you@example.com",
    phone="+91-XXXXXXXXXX",
    location="Bengaluru, India",
    about="Frontend/full-stack developer specializing in AI-powered conversational...",
    experience=[{
        "title": "Frontend Developer",
        "company": "Kapture CRM",
        "location": "Bengaluru, India",
        "start_date": "Jan 2024",
        "end_date": "Present",
        "bullets": [
            "Built and shipped the Vitos AI voice/chat agent builder UI end-to-end",
            "Normalized Redux state using createEntityAdapter across the Vitos state tree",
        ],
    }],
    projects=[{
        "name": "Shifra",
        "description": "Voice-controlled AI assistant built with xAI's Grok API.",
        "tech": ["React", "TypeScript", "Grok API", "Vercel"],
    }],
    skills=["React", "TypeScript", "Redux", "Node.js"],
)

Then: LinkedIn → Me → View Profile → Enhance profile → Import resume, review the auto-filled Experience/Education, and paste the About/Projects text manually. Since the file lands wherever the server runs, ask Claude/ChatGPT to send you the generated PDF or point output_path somewhere you can retrieve it (e.g. a synced folder), depending on how you've deployed the server.

Files

• server.py — remote MCP server (streamable-http + bearer auth) + tool definitions

• linkedin_client.py — LinkedIn REST API wrapper (userinfo, posts)

• resume_builder.py — reportlab-based resume PDF generator

• auth_setup.py — one-time OAuth2 flow to obtain a LinkedIn access token

• Dockerfile / Procfile — deployment

• .env.example — credential template

linkedIn-mcp