agentic-linkedin

The LinkedIn MCP that never breaks.

An MCP server that gives an AI agent everything a person can do on LinkedIn: you describe what you want, the agent executes it, and you never touch the site.

• Self-healing — selectors and endpoints live as data; provable fixes auto-apply when LinkedIn changes its DOM

• Session hygiene — human-like pacing, write budgets, health probes, graceful re-auth; never locks you out

• Reliable posting — verify-after-post, persisted dedupe keys, never an auto-retry: nothing double-posts

• Humanized content — per-user voice profiles and an AI-tell audit; everything reads like you wrote it

• Observable — plan-then-execute with field-level diffs and rendered previews; redacted failure artifacts

Install

npx agentic-linkedin

Requires Node 20+. The package is published to npm; the repository lives at github.com/DarkLvrd/linkedin-mcp.

Related MCP server: LinkedIn MCP Server

Sign in (once)

1. Start the server with your MCP client (Claude Desktop, Cursor, …).

2. Call the login tool. A browser window opens — sign in to LinkedIn and let it redirect to your feed.

3. The session persists to disk (~/.agentic-linkedin/session.json) and restores across restarts.

Identity is never stored in the repo or in failure artifacts. Cookie values are redacted from every log, tool output, and artifact by construction.

The workflow: plan-then-execute

Describe what you want. The agent calls plan with the intended writes and gets back an ordered list with previews:

• profile changes show field-level old → new diffs

• posts and messages show rendered feed-style previews with their AI-tell audit

• a raw toggle always exposes the exact arguments

Nothing executes until you approve the plan. reject cancels it. dry_run previews without storing anything. During execution every write still passes the pacing engine: per-sign-in budgets, the hourly ceiling, randomized human-like delays, and health probes — on a challenge the engine pauses until you re-authenticate.

Tools

• Session: session_status, login

• Reads: get_me, get_profile, get_posts, get_conversations, get_conversation_history, get_connections_summary, get_invitations, get_jobs, get_analytics

• Profile: update_profile, add_skill, remove_skill, reorder_skills, delete_ghost_entry

• Posts: create_post, edit_post, delete_post, comment, react

• Messaging: send_message (idempotency-keyed), recall_message, react_to_message

• Network: connect, respond_invitation, follow, endorse_skill, remove_connection

• Planning: plan, dry_run, approve, reject

• Self-healing: show_artifact, update_registry

• Voice: get_voice_profile, set_voice_profile, bootstrap_voice_profile, audit_draft

Configuration (environment variables)

Development

npm ci
npm run check   # typecheck + build + full test suite

Tests run fully offline: the transport seam is faked, Voyager/SDUI responses are replayed from recorded fixtures, and the binary is exercised end-to-end over stdio. Live LinkedIn actions are never automated in CI.

License

MIT — see LICENSE.